From 532bcad1e742df8e9b9943ef3c31dbf91bbd6713 Mon Sep 17 00:00:00 2001 From: Frank Solomon Date: Thu, 12 Sep 2024 12:40:03 -0700 Subject: [PATCH 01/13] Requested updates to cover CLI-related content . . . --- .../reference-yaml-feature-store.md | 34 +++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/articles/machine-learning/reference-yaml-feature-store.md b/articles/machine-learning/reference-yaml-feature-store.md index e30a20bfccb..7345dec16da 100644 --- a/articles/machine-learning/reference-yaml-feature-store.md +++ b/articles/machine-learning/reference-yaml-feature-store.md @@ -9,7 +9,7 @@ ms.topic: reference author: fbsolo-ms1 ms.author: franksolomon ms.reviewer: qiax -ms.date: 05/23/2023 +ms.date: 09/12/2024 ms.custom: cliv2, build-2023 --- @@ -21,7 +21,6 @@ ms.custom: cliv2, build-2023 ## YAML syntax - | Key | Type | Description | Allowed values | Default value | |--|--|--|--|--| | $schema | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including $schema at the top of your file enables you to invoke schema and resource completions. | | | @@ -82,6 +81,37 @@ tags: foo: bar ``` +## Configure the online store in the CLI with YAML + +```yaml +# online_store: + +target: /subscriptions/{sub-id}/resourceGroups/{rg}/XXX +type: redis +``` + +## Configure the online store in the CLI with Python + +```python +redis_arm_id = f"/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.Cache/Redis/{redis_name}" +online_store = MaterializationStore(type="redis", target=redis_arm_id) + +fs = FeatureStore( + name=featurestore_name, + location=location, + online_store=online_store, +) + +# wait for feature store creation +fs_poller = ml_client.feature_stores.begin_create(fs) + +# move the feature store to a YAML file + +yaml_path = root_dir + "/featurestore/featurestore_with_online.yaml" +fs.dump(yaml_path) + +``` + ## Next steps - [Install and use the CLI (v2)](how-to-configure-cli.md) From dcca6a9bbf0d08a2e59ff3edee0b51d082befeb2 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Thu, 12 Sep 2024 14:25:45 -0700 Subject: [PATCH 02/13] ptu updates: --- .../openai/how-to/provisioned-get-started.md | 24 +++++++++++++----- .../provisioned-throughput-onboarding.md | 15 +++++++---- .../media/provisioned/confirm-pricing.png | Bin 0 -> 92966 bytes .../media/provisioned/deployment-screen.png | Bin 196708 -> 167636 bytes .../ai-services/recover-purge-resources.md | 6 +++-- 5 files changed, 31 insertions(+), 14 deletions(-) create mode 100644 articles/ai-services/openai/media/provisioned/confirm-pricing.png diff --git a/articles/ai-services/openai/how-to/provisioned-get-started.md b/articles/ai-services/openai/how-to/provisioned-get-started.md index 5cdd4ad6a4b..5e67b5b3675 100644 --- a/articles/ai-services/openai/how-to/provisioned-get-started.md +++ b/articles/ai-services/openai/how-to/provisioned-get-started.md @@ -75,7 +75,16 @@ After you purchase a commitment on your quota, you can create a deployment. To c Important things to note: * The deployment dialog contains a reminder that you can purchase an Azure Reservation for Azure OpenAI Provisioned to obtain a significant discount for a term commitment. -* There is a message that tells you the list, hourly price of the deployment that you would be charged if this deployment is not covered by a reservation. This is a list price that does not include any negotiated discounts for your company. + +Once you have entered the deployment settings, click **Confirm Pricing** to continue. A pricing confirmation dialog will appear that will display the list price for the deployment, if you choose to pay for it on an hourly basis, with no Azure Reservation to provide a term discount. + +If you are unsure of the costs, cancel the deployment and proceed once you understand the payment model and underlying costs for provisioned deployment. This step may prevent unexpected, high charges on your payment invoice. Resources to educate yourself include: + +* [Azure Pricing Portal](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) +* [Understanding the provisioned throughput purchase model](provisioned-throughput-onboarding.md#understanding-the-provisioned-throughput-purchase-model) + + +:::image type="content" source="../media/provisioned/confirm-pricing.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment." lightbox="../media/provisioned/confirm-pricing.png"::: If you wish to create your deployment programmatically, you can do so with the following Azure CLI command. Update the `sku-capacity` with the desired number of provisioned throughput units. @@ -110,16 +119,17 @@ Things to notice: Selecting a resource and clicking **Switch resource** will cause the deployment dialog to redisplay using the selected resource. You can then proceed to create your deployment in the new region. -Learn more about the purchase model and how to purchase a reservation: - -* [Azure OpenAI provisioned onboarding guide](./provisioned-throughput-onboarding.md) -* [Guide for Azure OpenAI provisioned reservations](../concepts/provisioned-throughput.md) - ## Optionally purchase a reservation Following the creation of your deployment, you might want to purchase a term discount via an Azure Reservation. An Azure Reservation can provide a substantial discount on the hourly rate for users intending to use the deployment beyond a few days. -For more information on purchasing a reservation, see [Save costs with Microsoft Azure OpenAI service Provisioned Reservations](/azure/cost-management-billing/reservations/azure-openai). +For more information on the purchase model and reservations, see: +* [Save costs with Microsoft Azure OpenAI service provisioned reservations](/azure/cost-management-billing/reservations/azure-openai). +* [Azure OpenAI provisioned onboarding guide](./provisioned-throughput-onboarding.md) +* [Guide for Azure OpenAI provisioned reservations](../concepts/provisioned-throughput.md) + +> [!IMPORTANT] +> Capacity availability for model deployments is dynamic and changes frequently across regions and models. To prevent you from purchasing a reservation for more PTUs than you can use, create deployments first, and then purchase the Azure Reservation to cover the PTUs you have deployed. This best practice will ensure that you can take full advantage of the reservation discount and prevent you from purchasing a term commitment that you cannot use. ## Make your first inferencing calls The inferencing code for provisioned deployments is the same a standard deployment type. The following code snippet shows a chat completions call to a GPT-4 model. For your first time using these models programmatically, we recommend starting with our [quickstart guide](../quickstart.md). Our recommendation is to use the OpenAI library with version 1.0 or greater since this includes retry logic within the library. diff --git a/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md b/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md index 3de097406d6..c292a432c26 100644 --- a/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md +++ b/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md @@ -3,7 +3,7 @@ title: Azure OpenAI Service Provisioned Throughput Units (PTU) onboarding description: Learn about provisioned throughput units onboarding and Azure OpenAI. ms.service: azure-ai-openai ms.topic: conceptual -ms.date: 08/07/2024 +ms.date: 09/12/2024 manager: nitinme author: mrbullwinkle ms.author: mbullwin @@ -82,6 +82,9 @@ Customers that require long-term usage of provisioned deployments, however, migh > * The cost savings achieved by purchasing an Azure Reservation for Azure OpenAI Provisioned are significant, and it will be less expensive in many cases to maintain a deployment sized for full production volume paid for via a reservation than it would be to scale the deployment with incoming traffic. > * Having unused provisioned quota (PTUs) does not guarentee that capacity will be available to support increasing the size of the deployment when required. Quota limits the maximum number of PTUs that can be deployed, but it is not a capacity guarantee. Provisioned capacity for each region and modal dynamically changes throughout the day and might not be available when required. As a result, it is recommended to maintain a permanant deployment to cover your traffic needs (paid for via a reservation). +> [!NOTE] +> Charges for deployments on a deleted resource will continue until the resource is purged. To prevent this, delete a resource’s deployment before deleting the resource. For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). + ## Azure Reservations for Azure OpenAI Provisioned   Discounts on top of the hourly usage price can be obtained by purchasing an Azure Reservation for Azure OpenAI Provisioned. An Azure Reservation is a term-discounting mechanism shared by many Azure products. For example, Compute and Cosmos DB. For Azure OpenAI Provisioned, the reservation provides a discount for committing to payment for fixed number of PTUs for a one-month or one-year period.  @@ -103,9 +106,11 @@ Discounts on top of the hourly usage price can be obtained by purchasing an Azur * If the size of provisioned deployments within the scope of a reservation exceeds the amount of the reservation, the excess is charged at the hourly rate. For example, if deployments amounting to 250 PTUs exist within the scope of a 200 PTU reservation, 50 PTUs will be charged on an hourly basis until the deployment sizes are reduced to 200 PTUs, or a new reservation is created to cover the remaining 50. * Reservations guarantee a discounted price for the selected term.  They do not reserve capacity on the service or guarantee that it will be available when a deployment is created. It is highly recommended that customers create deployments prior to purchasing a reservation to prevent from over-purchasing a reservation. - -> [!NOTE] -> The Azure role and tenant policy requirements to purchase a reservation are different than those required to create a deployment or Azure OpenAI resource. See Azure OpenAI [Provisioned reservation documentation](https://aka.ms/oai/docs/ptum-reservations) for more details. + +> [!IMPORTANT] +> * Capacity availability for model deployments is dynamic and changes frequently across regions and models. To prevent you from purchasing a reservation for more PTUs than you can use, create deployments first, and then purchase the Azure Reservation to cover the PTUs you have deployed. This best practice will ensure that you can take full advantage of the reservation discount and prevent you from purchasing a term commitment that you cannot use. +> +> * The Azure role and tenant policy requirements to purchase a reservation are different than those required to create a deployment or Azure OpenAI resource. Verify authorization to purchase reservations in advance of needing to do so. See Azure OpenAI [Provisioned reservation documentation](https://aka.ms/oai/docs/ptum-reservations) for more details. ## Important: Sizing Azure OpenAI Provisioned Reservations @@ -119,7 +124,7 @@ To assist customers with purchasing the correct reservation amounts. The total n Managing Azure Reservations -After a reservation is created, it is a best practice monitor it to ensure it is receiving the usage you are expecting. This may be done via the Azure Reservation Portal or Azure Monitor. Details on these topics and others can be found here: +After a reservation is created, it is a best practice monitor it to ensure it is receiving the usage you are expecting. This can be done via the Azure Reservation Portal or Azure Monitor. Details on these topics and others can be found here: * [View Azure reservation utilization](/azure/cost-management-billing/reservations/reservation-utilization) * [View Azure Reservation purchase and refund transactions](/azure/cost-management-billing/reservations/view-purchase-refunds) diff --git a/articles/ai-services/openai/media/provisioned/confirm-pricing.png b/articles/ai-services/openai/media/provisioned/confirm-pricing.png new file mode 100644 index 0000000000000000000000000000000000000000..2386f5355a7a91ac0a6843f669f73e987ff6687f GIT binary patch literal 92966 zcmc$`WmFqc`|gcfkv5b9#Y=$V1zH?}dkMu0#VMs|af&;|t+-opx8hLTrNv!Zf(Hl? zf}P=c{_nfa$8$a%)(Rn$y)rv9_ujMT`dt@cYO3-?1kVZ3(9nn!6=c-W&@kM92Ra@O zu!X%QwF7uUcT$&^LaQ97-UVJ@eUf}DiH24a^XSF|8+eWXMd7^@8rn0ahX=Z%Iv9?I zc3-F{BdO_bc(8)~vry|C0#Bx6!%&}I0e*<4$0nl`7%AwdIj8BRNmiy=nH1Pw&=(uC zQ_OqZ@6@$h-*FE(*-eaQitJyD?lfPv)m$!?T`pdW9lO&IX@<$Xh*JQA!WjJT_ixf4 z{sJ~oU<__^{b#d`DIP;Y>;Km#Nag51?<;^q9`iOU|4(D7|C7en>yY~^==o*meLD*H zwer<`IdHA^ZfWgK`*z>xeou7TlTe20zr8_)I_wdVfnu_6wpnKEea_P32jBAaJ)N5_ z)zo=w8`o`gkD9YOYTl{*k?n(e5iu18x!E9@(YJ5cy8X-TTkOf_8zOJMYZo z({oG`nevkmu6jAa@2W$XEm+J zQt?dJ*ndwW8>P9U74|NwD9fW%_zt-h!SOG}EZV9Yo1FU4b>QEW1c{0gaA33cNVAbL zI|~oBAi_hh@yBh=yQu}a8Bvx16>kOaT$mm)8i-jxv=-R|j>xvhRJ5(W-?DX>$ojQaZyzx%eUf9YjF zVKty;s(iEz+&6+}+Rnz8zVgQ@L@FXi5|nT#5R5UR>Y&z-N$-CMpN;aBMR2tg3!ned zDEkeCw6|=>E0+QdYQd^z1eSHLZL%{9h%-x7l89JR&EwLEpo)%9W&q$1S=dlSBQPKulV((OB}HiF5yJdCTfm|eqUCN?DN>26{2OvEW& zETFLNKyengi|Q4}5zd*j35Zer51>{%g!jwqN?qm*n)J-$WJ{a>rIj5R)g1g?ex`53 z(|=J@@=DHM9c_Z>_ZF8FIqZc2g@$nI!#vYlER|hL-F*U_gF%Wjx6Y}LHe)J=2fArkm*H3Afgeo`Q34b7J> z^qIT}EMiM@WFf7Na{d~48%LldVIQs7^)y|3&aBh>sS1CVbykip-Ic{@0 zzq>w8-XB%sq~fgU1r?U=_)>L2E}JJ3KaLb@XMOpF|B=H z6a`NnY5;Qgj#kheF(qGi*tk^5@xBz~a}r@x?gPv{iFYl5*Y?1)pzfkcbG9D-xBT{U ztut~ZrE%GDWUu4;h<$F>1R5E>9L`>;>y8AfRqp=L%~KatXVJzC`G z3@E3+f+^*?zXXWeWCc2YJB*BvK~lHEpi9K{F5^Nw-)+&%Bjh~~8+4XRLvFL(b7Hol zMJhF)rf+P}9X`LWn-E|6is(e`ie%1&Si|=0S`W1^noys!(K@F66Seh9KJ;{OD9<@8 z*)}Ee4_+R%9v_LK*0vUl>dXg@PCSFjv|o-mjJ3=XdjBqxCKf7)Odu3kasItGE^^G_ zIz=}B52!62+xjUv+0GT~h3_L6sGR=w>F5Q1V5gO=%|AxZ*jX%Y%L-TP*WGa9c&2%WNHaEj-9> zZ%}icT*48TtS0l1+Q)4(W;wad({F#`uemX9#yYb1$HZA{-PuVLQ zoXIh_(;giI7p1(Q&kaBdYa$c)%q>4-uA*F?Gn7bIwXo#a8FDwS9JTU8{n zh+PPW3eMz59bagm`j);##DRFo4PEwC<1+i_voo@2u^V{L46Ww@&vZ@k zPD__jP&xm8wgqfLo;R3FQ2}iI_;rc7V_W4$5aenInnch2^*o5{)91D90^A932nd3| z%MDcIw6+c&3c=L;xGPG_Pk>l7wqB79tL0zNi~d|+#kAfG>Ztrx*BiPot35A4jQBY< zald6q-)ix%#KkSH@Hn=A4eiSK&0I1_g3L9Mdl?38r$_# z4tUQe4n=xf*>oWn8?fD-=M3DmT8}$C6)770@B3Nb$NmgH`m-TC)B-m2MHrtBv2a5w z&Vcg%j=%J|C{sJWQ)2SaP?)vEf^j<>ViTysWHmPQmECeca3ST^j~R?0$rh6_RtFD7 zVI>-;rMizuxS#H{B{eP*?wF)FSW+$SQ7nDjC4LoldCuAo{51wCL;Y&+H|g)6qNbg zz|<~;*ez0{KuVK{X|j<3)nSY8E_NHmMjS(`!I?mv#!2wQ!jnxhBcNc=CNY2Gm5^&J zMRC^`C5)iGWbl`U8Etmri5@D9v|ck{dBGh$Kw4vwz4$wOp?#rzPv@X!t2e5TVph+( zVlzijWc>Fi#n>WQkeHU0eX9AuJhq0vbO75EwJY9dJ6JY~NkK~0>Ml}M?^}olGgu4> z7Lk)=ll0Y!LG_<+N)0iRDC_Ptaq7dg%`RGP&GWb+w=et@#NjhSX79$j85IL!+COd6 zQ+&CIf!w*5usOgl*zJC9gfQurnXhMCaZ=)I9wpn%E}=P>H7Yp6HKiC!`m7Pb%W(Td zEp66;Nmf5pEOzrDFpmUxmSP4JSUr7yE>BDQ%@Q{(lZ|*`gQ1x!P$}Gsj5an{1~{?V z9AQ{Al10OD+XN)A*zInLgBfXdpBDK(%*l{WAH6_oO+Aql|4c|h6y`_3xR9d}AdL&wwUNgTJ=oK7MV06&UQR_M397mt z0M?EGV|jK6E>{XYKg=_6@-`E!`(ni7l9UA&e7vjWcWKt*G#iO3_3ZceJ+xS$ZXqZOlJcwnPyh}4e#hxjLMwZ^LNh1_ zDx*>U>&^gEkeH-@FU^>x_<|!J|3gwYcpLf8mki$@42sS2TV`^LlljeXI!Ak(Gk{8< z&q*Nm2nYFMD=0QwG0;dS?9m9lmi7btDrfu9VsygfMAq|-S&Nna&ErmfMIh~_{Z)-g zL5j7BUZvIR`9YC)B``{sMKIB}*kgK94_QN-_0thXmO`Z(1EP9de9JVPWo~{P-o;+Pt%L*S!Au12>c_pSTf* zrv&i=hw+b=5(nSA3hb#4NUp_ED_O#sN>yEoH}Sp&7(yjj;96+O z+sfh+r+HRZ$Uwl-r!G$LwoGd}c)tjf;`8Leq58+9|- zjU{>3%U%)^RQ-%YG7%1^TG9KVcGD(W!aEIS&Dma882D=KV)WcCT9;22H+1Ti?{|$f zd%&YS&D{|xmeo}F4L&PHzo^>kd^J9<%n!+btU7Z0P$gwcn0p-^N9PeWjCjKS?*^2G8}|*Y+4<*@R^V4V_o47UYTHW9S*IW!xah;><7VU*sEg_;YYV z1=fnh>iKo6XhhKv44R2i?-c~6C{tDFoBM)ASTcs~IE@Fm6 zn)io~Z?W`t3(exgw0AJ#kFrk55v+>U1xzp{jQQ{F8S#WO3?m=>(XKkU^ zyA3+3znI>$kbBCT1?Lj4d6w-|FzYYki8bE-(x|9#-!c2zmZc(PkAi|hf#lx9TwoEl zr==7ip7e801{X2}M*Jf(F8>i)+B%qUeK%`qUP?o008)gxd*d9;N@<`*c2aV-bRY1o zkPY82M_$zh;YxkZFPf+_n~xcg@R7xTz%lu6{y&1K(!k+O2?jP6_af6jrM4}ls23+s zbM&=TJ*aXd^-m^)S)9E&q-#|4aNjs^Ui-(k7gTATerj$K zk(r((T`$^8^4UTU6rQ9{wQcl$J)6wT$+tv2wP%)E+dK^32p`s>ao-g5S!kKe`18l# zPV1dxV|K;NH2;0!F9oAPZgVU)r_u~(?Q;&=;@WyUc1dViwm15kUQyqv1N-Wg?gfpC z4<~TWsZr(&)Q#^Oh+3$_%tvv|AT?8>>0~m3f-&n(#3`SrQF+GRoEMS4QmJM`NHGw>i)^4R$~ z8aq#p0KzJVKIgZxvJq|0N&3h0pdi{MLHm-Z4v)uqGmZvE^iQS*d=j#8qrrBiTcr|l z9JQEmDrZGu$m(L|~-788oTvQ$AuGC61`Pi!oI^KQ`ewr<2X?~m) z-Vs)Lf)lxLY@(DANn_6xmZh3l9z7fs?%P1f^|i1HVWNqiJvihJ)3@LLt^k(fL>+t- zRD?cFLnnyL$j%mnKV=C ziAZ?nsaF#jX~`L`;y645CP?Gr(TC1~`}q$TF}?XS9a*bVvcI4bx~3p{&cG{tT<8Ld zMga2S;FCHy9TWdlTMJ&0A0($W|GSSr_%{($1vS>?|L2iyYWbv*g_y$v~ zd^QM+lo;wjtQiYPUc{*j*09g=TFj1-!}=1S;^&Md)z1651klP5QJ8ro+HKnIJz*a^F&GtCt-c)qIrQ$JHK;wg7_*ErL7mf-p8&u7ywG84Z)Z@s=bh_K#m z#>ECB5M41RrQhE4+=8T_o_mjsU=`VaYPh3S6>D1pnUq^{q{v?*<4jt*aBh+q3^z-P z9McC(2-$*|nc}5AY<_7$G32oag=i)6ln?q7dYrL4VM#`v;cMiFB(P2;hOG4R>r7~; 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a/articles/ai-services/recover-purge-resources.md b/articles/ai-services/recover-purge-resources.md index 0f89b12c422..8c9a88edf9b 100644 --- a/articles/ai-services/recover-purge-resources.md +++ b/articles/ai-services/recover-purge-resources.md @@ -6,7 +6,7 @@ author: eric-urban manager: nitinme ms.service: azure-ai-services ms.topic: how-to -ms.date: 11/15/2023 +ms.date: 09/12/2024 ms.author: eur --- @@ -17,7 +17,9 @@ This article provides instructions on how to recover or purge an Azure AI servic Once you delete a resource, you won't be able to create another one with the same name for 48 hours. To create a resource with the same name, you need to purge the deleted resource. > [!NOTE] -> The instructions in this article are applicable to both a multi-service resource and a single-service resource. A multi-service resource enables access to multiple Azure AI services using a single key and endpoint. On the other hand, a single-service resource enables access to just that specific Azure AI service for which the resource was created. +> * The instructions in this article are applicable to both a multi-service resource and a single-service resource. A multi-service resource enables access to multiple Azure AI services using a single key and endpoint. On the other hand, a single-service resource enables access to just that specific Azure AI service for which the resource was created. +> +> * Charges for provisioned deployments on a deleted resource will continue until the resource is purged. To prevent this, delete a resource's deployment before deleting the resource. ## Recover a deleted resource From 2a015e23537c1f028ff75136a85e5fb7a7846506 Mon Sep 17 00:00:00 2001 From: Mope Akande <17515964+msakande@users.noreply.github.com> Date: Thu, 12 Sep 2024 16:30:50 -0500 Subject: [PATCH 03/13] Revert "Fix response format for chat completions models (AI Studio)" --- .../ai-studio/how-to/deploy-models-jais.md | 22 +++++----- .../ai-studio/how-to/deploy-models-llama.md | 22 +++++----- .../how-to/deploy-models-mistral-nemo.md | 20 +++++----- .../how-to/deploy-models-mistral-open.md | 40 +++++++++---------- .../ai-studio/how-to/deploy-models-mistral.md | 20 +++++----- .../how-to/deploy-models-phi-3-5-moe.md | 6 +-- .../how-to/deploy-models-phi-3-vision.md | 6 +-- .../ai-studio/how-to/deploy-models-phi-3.md | 6 +-- .../ai-studio/how-to/fine-tune-model-llama.md | 2 +- 9 files changed, 74 insertions(+), 70 deletions(-) diff --git a/articles/ai-studio/how-to/deploy-models-jais.md b/articles/ai-studio/how-to/deploy-models-jais.md index 9686a0c62a5..b1831700770 100644 --- a/articles/ai-studio/how-to/deploy-models-jais.md +++ b/articles/ai-studio/how-to/deploy-models-jais.md @@ -201,7 +201,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -214,12 +214,12 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` > [!WARNING] -> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -482,7 +482,7 @@ var response = await client.path("/chat/completions").post({ ``` > [!WARNING] -> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -580,7 +580,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip ### The inference package installed -You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites: +You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites: * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. @@ -606,7 +606,7 @@ using Azure.Identity; using Azure.AI.Inference; ``` -This example also uses the following namespaces but you may not always need them: +This example also use the following namespaces but you may not always need them: ```csharp @@ -775,7 +775,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); ``` > [!WARNING] -> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -1088,7 +1088,7 @@ Explore other parameters that you can specify in the inference client. For a ful ``` > [!WARNING] -> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -1165,14 +1165,14 @@ The following example shows how to handle events when the model detects harmful ## More inference examples -For more examples of how to use Jais models, see the following examples and tutorials: +For more examples of how to use Jais, see the following examples and tutorials: | Description | Language | Sample | |-------------------------------------------|-------------------|-----------------------------------------------------------------| | Azure AI Inference package for JavaScript | JavaScript | [Link](https://aka.ms/azsdk/azure-ai-inference/javascript/samples) | | Azure AI Inference package for Python | Python | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples) | -## Cost and quota considerations for Jais models deployed as serverless API endpoints +## Cost and quota considerations for Jais family of models deployed as serverless API endpoints Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios. @@ -1189,4 +1189,4 @@ For more information on how to track costs, see [Monitor costs for models offere * [Deploy models as serverless APIs](deploy-models-serverless.md) * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) -* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) +* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) \ No newline at end of file diff --git a/articles/ai-studio/how-to/deploy-models-llama.md b/articles/ai-studio/how-to/deploy-models-llama.md index 1301a39599f..8cdc9350477 100644 --- a/articles/ai-studio/how-to/deploy-models-llama.md +++ b/articles/ai-studio/how-to/deploy-models-llama.md @@ -255,7 +255,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -268,12 +268,12 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` > [!WARNING] -> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -610,7 +610,7 @@ var response = await client.path("/chat/completions").post({ ``` > [!WARNING] -> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -765,7 +765,7 @@ For deployment to a self-hosted managed compute, you must have enough quota in y ### The inference package installed -You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites: +You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites: * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. @@ -791,7 +791,7 @@ using Azure.Identity; using Azure.AI.Inference; ``` -This example also uses the following namespaces but you may not always need them: +This example also use the following namespaces but you may not always need them: ```csharp @@ -973,7 +973,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); ``` > [!WARNING] -> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -1348,7 +1348,7 @@ Explore other parameters that you can specify in the inference client. For a ful ``` > [!WARNING] -> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -1441,7 +1441,7 @@ The following example shows how to handle events when the model detects harmful ## More inference examples -For more examples of how to use Meta Llama models, see the following examples and tutorials: +For more examples of how to use Meta Llama, see the following examples and tutorials: | Description | Language | Sample | |-------------------------------------------|-------------------|------------------------------------------------------------------- | @@ -1453,7 +1453,7 @@ For more examples of how to use Meta Llama models, see the following examples an | LangChain | Python | [Link](https://aka.ms/meta-llama-3.1-405B-instruct-langchain) | | LiteLLM | Python | [Link](https://aka.ms/meta-llama-3.1-405B-instruct-litellm) | -## Cost and quota considerations for Meta Llama models deployed as serverless API endpoints +## Cost and quota considerations for Meta Llama family of models deployed as serverless API endpoints Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios. @@ -1463,7 +1463,7 @@ Each time a project subscribes to a given offer from the Azure Marketplace, a ne For more information on how to track costs, see [Monitor costs for models offered through the Azure Marketplace](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace). -## Cost and quota considerations for Meta Llama models deployed to managed compute +## Cost and quota considerations for Meta Llama family of models deployed to managed compute Meta Llama models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration. diff --git a/articles/ai-studio/how-to/deploy-models-mistral-nemo.md b/articles/ai-studio/how-to/deploy-models-mistral-nemo.md index 9a97f8c98cf..8367b3a1b25 100644 --- a/articles/ai-studio/how-to/deploy-models-mistral-nemo.md +++ b/articles/ai-studio/how-to/deploy-models-mistral-nemo.md @@ -5,7 +5,7 @@ description: Learn how to use Mistral Nemo chat model with Azure AI Studio. ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/08/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -209,7 +209,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -222,7 +222,7 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` @@ -234,13 +234,15 @@ Mistral Nemo chat model can create JSON outputs. Set `response_format` to `json_ ```python +from azure.ai.inference.models import ChatCompletionsResponseFormatJSON + response = client.complete( messages=[ SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using." " the following format: { ""answer"": ""response"" }."), UserMessage(content="How many languages are in the world?"), ], - response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT } + response_format=ChatCompletionsResponseFormatJSON() ) ``` @@ -960,7 +962,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip ### The inference package installed -You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites: +You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites: * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. @@ -986,7 +988,7 @@ using Azure.Identity; using Azure.AI.Inference; ``` -This example also uses the following namespaces but you may not always need them: +This example also use the following namespaces but you may not always need them: ```csharp @@ -2008,7 +2010,7 @@ The following example shows how to handle events when the model detects harmful ## More inference examples -For more examples of how to use Mistral models, see the following examples and tutorials: +For more examples of how to use Mistral, see the following examples and tutorials: | Description | Language | Sample | |-------------------------------------------|-------------------|-----------------------------------------------------------------| @@ -2022,7 +2024,7 @@ For more examples of how to use Mistral models, see the following examples and t | LiteLLM | Python | [Link](https://aka.ms/mistral-large/litellm-sample) | -## Cost and quota considerations for Mistral models deployed as serverless API endpoints +## Cost and quota considerations for Mistral family of models deployed as serverless API endpoints Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios. @@ -2039,4 +2041,4 @@ For more information on how to track costs, see [Monitor costs for models offere * [Deploy models as serverless APIs](deploy-models-serverless.md) * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) -* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) +* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) \ No newline at end of file diff --git a/articles/ai-studio/how-to/deploy-models-mistral-open.md b/articles/ai-studio/how-to/deploy-models-mistral-open.md index 07e41e059e2..20aa4714814 100644 --- a/articles/ai-studio/how-to/deploy-models-mistral-open.md +++ b/articles/ai-studio/how-to/deploy-models-mistral-open.md @@ -5,7 +5,7 @@ description: Learn how to use Mistral-7B and Mixtral chat models with Azure AI S ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/08/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -63,8 +63,8 @@ Mixtral 8x22B comes with the following strengths: * Fluent in English, French, Italian, German, and Spanish * Strong mathematics and coding capabilities -* Natively capable of function calling, enabling application development and tech stack modernization at scale -* Precise information recall from large documents, due to its 64K tokens context window +* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale +* Pprecise information recall from large documents, due to its 64K tokens context window The following models are available: @@ -257,7 +257,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -270,12 +270,12 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` > [!WARNING] -> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -352,8 +352,8 @@ Mixtral 8x22B comes with the following strengths: * Fluent in English, French, Italian, German, and Spanish * Strong mathematics and coding capabilities -* Natively capable of function calling, enabling application development and tech stack modernization at scale -* Precise information recall from large documents, due to its 64K tokens context window +* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale +* Pprecise information recall from large documents, due to its 64K tokens context window The following models are available: @@ -576,7 +576,7 @@ var response = await client.path("/chat/completions").post({ ``` > [!WARNING] -> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -658,8 +658,8 @@ Mixtral 8x22B comes with the following strengths: * Fluent in English, French, Italian, German, and Spanish * Strong mathematics and coding capabilities -* Natively capable of function calling, enabling application development and tech stack modernization at scale -* Precise information recall from large documents, due to its 64K tokens context window +* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale +* Pprecise information recall from large documents, due to its 64K tokens context window The following models are available: @@ -689,7 +689,7 @@ For deployment to a self-hosted managed compute, you must have enough quota in y ### The inference package installed -You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites: +You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites: * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. @@ -715,7 +715,7 @@ using Azure.Identity; using Azure.AI.Inference; ``` -This example also uses the following namespaces but you may not always need them: +This example also use the following namespaces but you may not always need them: ```csharp @@ -897,7 +897,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); ``` > [!WARNING] -> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -976,8 +976,8 @@ Mixtral 8x22B comes with the following strengths: * Fluent in English, French, Italian, German, and Spanish * Strong mathematics and coding capabilities -* Natively capable of function calling, enabling application development and tech stack modernization at scale -* Precise information recall from large documents, due to its 64K tokens context window +* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale +* Pprecise information recall from large documents, due to its 64K tokens context window The following models are available: @@ -1230,7 +1230,7 @@ Explore other parameters that you can specify in the inference client. For a ful ``` > [!WARNING] -> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. +> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). @@ -1279,7 +1279,7 @@ The following extra parameters can be passed to Mistral-7B and Mixtral chat mode ## More inference examples -For more examples of how to use Mistral models, see the following examples and tutorials: +For more examples of how to use Mistral, see the following examples and tutorials: | Description | Language | Sample | |-------------------------------------------|-------------------|-----------------------------------------------------------------| @@ -1293,7 +1293,7 @@ For more examples of how to use Mistral models, see the following examples and t | LiteLLM | Python | [Link](https://aka.ms/mistral-large/litellm-sample) | -## Cost and quota considerations for Mistral models deployed to managed compute +## Cost and quota considerations for Mistral family of models deployed to managed compute Mistral models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration. @@ -1306,4 +1306,4 @@ It is a good practice to start with a low number of instances and scale up as ne * [Deploy models as serverless APIs](deploy-models-serverless.md) * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) -* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) +* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) \ No newline at end of file diff --git a/articles/ai-studio/how-to/deploy-models-mistral.md b/articles/ai-studio/how-to/deploy-models-mistral.md index 7b8b6fcd1c6..ba88312a4da 100644 --- a/articles/ai-studio/how-to/deploy-models-mistral.md +++ b/articles/ai-studio/how-to/deploy-models-mistral.md @@ -5,7 +5,7 @@ description: Learn how to use Mistral premium chat models with Azure AI Studio. ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/08/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -239,7 +239,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -252,7 +252,7 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` @@ -264,13 +264,15 @@ Mistral premium chat models can create JSON outputs. Set `response_format` to `j ```python +from azure.ai.inference.models import ChatCompletionsResponseFormatJSON + response = client.complete( messages=[ SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using." " the following format: { ""answer"": ""response"" }."), UserMessage(content="How many languages are in the world?"), ], - response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT } + response_format=ChatCompletionsResponseFormatJSON() ) ``` @@ -1050,7 +1052,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip ### The inference package installed -You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites: +You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites: * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. @@ -1076,7 +1078,7 @@ using Azure.Identity; using Azure.AI.Inference; ``` -This example also uses the following namespaces but you may not always need them: +This example also use the following namespaces but you may not always need them: ```csharp @@ -2128,7 +2130,7 @@ The following example shows how to handle events when the model detects harmful ## More inference examples -For more examples of how to use Mistral models, see the following examples and tutorials: +For more examples of how to use Mistral, see the following examples and tutorials: | Description | Language | Sample | |-------------------------------------------|-------------------|-----------------------------------------------------------------| @@ -2142,7 +2144,7 @@ For more examples of how to use Mistral models, see the following examples and t | LiteLLM | Python | [Link](https://aka.ms/mistral-large/litellm-sample) | -## Cost and quota considerations for Mistral models deployed as serverless API endpoints +## Cost and quota considerations for Mistral family of models deployed as serverless API endpoints Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios. @@ -2159,4 +2161,4 @@ For more information on how to track costs, see [Monitor costs for models offere * [Deploy models as serverless APIs](deploy-models-serverless.md) * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) -* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) +* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) \ No newline at end of file diff --git a/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md b/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md index 9409484ca3e..8c167738f7b 100644 --- a/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md +++ b/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md @@ -5,7 +5,7 @@ description: Learn how to use Phi-3.5 MoE chat model with Azure AI Studio. ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/19/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -219,7 +219,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -232,7 +232,7 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` diff --git a/articles/ai-studio/how-to/deploy-models-phi-3-vision.md b/articles/ai-studio/how-to/deploy-models-phi-3-vision.md index d0c068804a2..2542fb3e05b 100644 --- a/articles/ai-studio/how-to/deploy-models-phi-3-vision.md +++ b/articles/ai-studio/how-to/deploy-models-phi-3-vision.md @@ -5,7 +5,7 @@ description: Learn how to use Phi-3 chat model with vision with Azure AI Studio. ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/19/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -215,7 +215,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -228,7 +228,7 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` diff --git a/articles/ai-studio/how-to/deploy-models-phi-3.md b/articles/ai-studio/how-to/deploy-models-phi-3.md index dda83c5515a..849f972e8a0 100644 --- a/articles/ai-studio/how-to/deploy-models-phi-3.md +++ b/articles/ai-studio/how-to/deploy-models-phi-3.md @@ -5,7 +5,7 @@ description: Learn how to use Phi-3 family chat models with Azure AI Studio. ms.service: azure-ai-studio manager: scottpolly ms.topic: how-to -ms.date: 09/12/2024 +ms.date: 08/19/2024 ms.reviewer: kritifaujdar reviewer: fkriti ms.author: mopeakande @@ -256,7 +256,7 @@ print_stream(result) Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). ```python -from azure.ai.inference.models import ChatCompletionsResponseFormat +from azure.ai.inference.models import ChatCompletionsResponseFormatText response = client.complete( messages=[ @@ -269,7 +269,7 @@ response = client.complete( stop=["<|endoftext|>"], temperature=0, top_p=1, - response_format={ "type": ChatCompletionsResponseFormat.TEXT }, + response_format=ChatCompletionsResponseFormatText(), ) ``` diff --git a/articles/ai-studio/how-to/fine-tune-model-llama.md b/articles/ai-studio/how-to/fine-tune-model-llama.md index 54852ce8878..60c9014f746 100644 --- a/articles/ai-studio/how-to/fine-tune-model-llama.md +++ b/articles/ai-studio/how-to/fine-tune-model-llama.md @@ -230,7 +230,7 @@ To fine-tune a Llama 2 model in an existing Azure AI Studio project, follow thes :::image type="content" source="../media/how-to/fine-tune/llama/llama-pay-as-you-go-overview.png" alt-text="Screenshot of pay-as-you-go marketplace overview." lightbox="../media/how-to/fine-tune/llama/llama-pay-as-you-go-overview.png"::: -1. Choose a base model to fine-tune and select **Confirm**. Your choice influences both the performance and [the cost of your model](./deploy-models-llama.md#cost-and-quota-considerations-for-meta-llama-models-deployed-as-serverless-api-endpoints). +1. Choose a base model to fine-tune and select **Confirm**. Your choice influences both the performance and [the cost of your model](./deploy-models-llama.md#cost-and-quota-considerations-for-meta-llama-family-of-models-deployed-as-serverless-api-endpoints). :::image type="content" source="../media/how-to/fine-tune/llama/fine-tune-select-model.png" alt-text="Screenshot of option to select a model to fine-tune." lightbox="../media/how-to/fine-tune/llama/fine-tune-select-model.png"::: From 1adf40c2a9d06064365038cc79f8255fee7218c3 Mon Sep 17 00:00:00 2001 From: mrbullwinkle <31510320+mrbullwinkle@users.noreply.github.com> Date: Thu, 12 Sep 2024 17:40:55 -0400 Subject: [PATCH 04/13] update --- articles/ai-services/openai/whats-new.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/whats-new.md b/articles/ai-services/openai/whats-new.md index ca84f2e75cc..fe782002d6d 100644 --- a/articles/ai-services/openai/whats-new.md +++ b/articles/ai-services/openai/whats-new.md @@ -42,7 +42,7 @@ Request access: [limited access model application](https://aka.ms/oai/modelacces ### Limitations -The `o1-preview` and `o1-mini` models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the GPT-4o and GPT-4o-mini models. For many tasks, the generally available GPT-4o models may still be more suitable. +The `o1` models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the latest GPT-4o model. For many tasks, the generally available GPT-4o models may still be more suitable. ### Safety From a85b6b7d2cda707d715eec07eb59996140599631 Mon Sep 17 00:00:00 2001 From: mrbullwinkle <31510320+mrbullwinkle@users.noreply.github.com> Date: Thu, 12 Sep 2024 17:45:01 -0400 Subject: [PATCH 05/13] update --- articles/ai-services/openai/whats-new.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/whats-new.md b/articles/ai-services/openai/whats-new.md index fe782002d6d..bd3ad049eef 100644 --- a/articles/ai-services/openai/whats-new.md +++ b/articles/ai-services/openai/whats-new.md @@ -42,7 +42,7 @@ Request access: [limited access model application](https://aka.ms/oai/modelacces ### Limitations -The `o1` models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the latest GPT-4o model. For many tasks, the generally available GPT-4o models may still be more suitable. +The `o1` series models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the latest GPT-4o model. For many tasks, the generally available GPT-4o models may still be more suitable. ### Safety From b5fcf0cd74e2049b9d4aab292ccab488bb3afcb4 Mon Sep 17 00:00:00 2001 From: mrbullwinkle <31510320+mrbullwinkle@users.noreply.github.com> Date: Thu, 12 Sep 2024 17:48:12 -0400 Subject: [PATCH 06/13] update --- articles/ai-services/openai/whats-new.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/whats-new.md b/articles/ai-services/openai/whats-new.md index bd3ad049eef..58366f3349b 100644 --- a/articles/ai-services/openai/whats-new.md +++ b/articles/ai-services/openai/whats-new.md @@ -42,7 +42,7 @@ Request access: [limited access model application](https://aka.ms/oai/modelacces ### Limitations -The `o1` series models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the latest GPT-4o model. For many tasks, the generally available GPT-4o models may still be more suitable. +The `o1` series models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs which are available in the latest GPT-4o model. For many tasks, the generally available GPT-4o models may still be more suitable. ### Safety From b30480fdb887c687210ee1f52ccd4e1620988b3c Mon Sep 17 00:00:00 2001 From: Frank Solomon Date: Thu, 12 Sep 2024 14:53:26 -0700 Subject: [PATCH 07/13] Edits, changes, and updates . . . --- .../reference-yaml-feature-store.md | 31 +++++++++++++++---- ...torial-online-materialization-inference.md | 6 ++-- 2 files changed, 29 insertions(+), 8 deletions(-) diff --git a/articles/machine-learning/reference-yaml-feature-store.md b/articles/machine-learning/reference-yaml-feature-store.md index 7345dec16da..e76669b5c57 100644 --- a/articles/machine-learning/reference-yaml-feature-store.md +++ b/articles/machine-learning/reference-yaml-feature-store.md @@ -26,11 +26,14 @@ ms.custom: cliv2, build-2023 | $schema | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including $schema at the top of your file enables you to invoke schema and resource completions. | | | | name | string | **Required.** Name of the feature store. | | | | compute_runtime | object | The compute runtime configuration used for materialization job. | | | -| compute_runtime.spark_runtime_version | string | The Azure Machine Learning Spark runtime version. | 3.2 | 3.2 | +| compute_runtime.spark_runtime_version | string | The Azure Machine Learning Spark runtime version. | 3.4 | 3.4 | | offline_store | object | | | | | offline_store.type | string | **Required** if offline_store is provided. The type of offline store. Only data lake gen2 type of storage is supported. | azure_data_lake_gen2 | | | offline_store.target | string | **Required** if offline_store is provided. The datalake Gen2 storage URI in the format of `/subscriptions//resourceGroups//providers/Microsoft.Storage/storageAccounts//blobServices/default/containers/`. | | | -| materialization_identity | object | The user-assigned managed identity that used for the materialization job. This identity needs to be granted necessary roles to access Feature Store service, the data source and the offline storage. | | | +| online_store | object | | | | +| online_store.type | string | **Required** if online_store is provided. The type of online store. Only redis cache is supported. | redis | | +| online_store.target | string | **Required** if online_store is provided. The Redis Cache URI in the format of `/subscriptions//resourceGroups//providers/Microsoft.Cache/Redis/`. | | | +| materialization_identity | object | The user-assigned managed identity that used for the materialization job. This identity needs to be granted necessary roles to access Feature Store service, the data source, and the offline storage. | | | | materialization_identity.client_id | string | The client ID for your user-assigned managed identity. | | | | materialization_identity.resource_id | string | The resource ID for your user-assigned managed identity. | | | | materialization_identity.principal_id | string | the principal ID for your user-assigned managed identity.| | | @@ -47,7 +50,7 @@ You can include other [workspace properties](reference-yaml-workspace.md). The `az ml feature-store` command can be used for managing Azure Machine Learning feature store workspaces. ## Examples -Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli). Some common examples are shown in below. +Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli). Some common examples are shown here: ## YAML basic @@ -84,10 +87,26 @@ tags: ## Configure the online store in the CLI with YAML ```yaml -# online_store: +$schema: http://azureml/sdk-2-0/FeatureStore.json +name: mktg-feature-store + +compute_runtime: + spark_runtime_version: 3.4 + +online_store: + type: redis + target: "/subscriptions//resourceGroups//providers/Microsoft.Cache/Redis/" + +materialization_identity: + client_id: 00001111-aaaa-2222-bbbb-3333cccc4444 + principal_id: aaaaaaaa-bbbb-cccc-1111-222222222222 + resource_id: /subscriptions//resourceGroups//providers/Microsoft.ManagedIdentity/userAssignedIdentities/ -target: /subscriptions/{sub-id}/resourceGroups/{rg}/XXX -type: redis +# Many of workspace parameters will also be supported: +location: eastus +display_name: marketing feature store +tags: + foo: bar ``` ## Configure the online store in the CLI with Python diff --git a/articles/machine-learning/tutorial-online-materialization-inference.md b/articles/machine-learning/tutorial-online-materialization-inference.md index 68c02fe8926..58372f38f4d 100644 --- a/articles/machine-learning/tutorial-online-materialization-inference.md +++ b/articles/machine-learning/tutorial-online-materialization-inference.md @@ -109,6 +109,8 @@ The feature store needs the Azure Cache for Redis as an attached resource, for u > - Online store target > - Offline store target +For an example that shows how to do this with the SDK, visit the [Tutorial: Different Approaches for Provisioning a Managed Feature Store](https://github.com/Azure/azureml-examples/blob/main/sdk/python/featurestore_sample/notebooks/sdk_and_cli/4.Provision-feature-store.ipynb) resource. + ## Materialize the `accounts` feature set data to online store ### Enable materialization on the `accounts` feature set @@ -166,11 +168,11 @@ You can explore the feature materialization status for a feature set from the ** - Your data can have a maximum of 2,000 *data intervals*. If your data contains more than 2,000 *data intervals*, create a new feature set version. - You can provide a list of more than one data statuses (for example, `["None", "Incomplete"]`) in a single backfill job. - During backfill, a new materialization job is submitted for each *data interval* that falls in the defined feature window. -- A new job is not submitted for a *data interval* if a materialization job is already pending, or is running for a *data interval* that hasn't yet been backfilled. +- A new job isn't submitted for a *data interval* if a materialization job is already pending, or is running for a *data interval* that hasn't yet been backfilled. ### Updating online materialization store - If an online materialization store is to be updated at the feature store level, then all feature sets in the feature store should have online materialization disabled. -- If online materialization is disabled on a feature set, the materialization status of the already-materialized data in the online materialization store will be reset. This renders the already-materialized data unusable. You must resubmit your materialization jobs after you enable online materialization. +- If online materialization is disabled on a feature set, the materialization status of the already-materialized data in the online materialization store is reset. This renders the already-materialized data unusable. You must resubmit your materialization jobs after you enable online materialization. - If only offline materialization was initially enabled for a feature set, and online materialization is enabled later: - The default data materialization status of the data in the online store will be `None`. - When the first online materialization job is submitted, the data already materialized in the offline store, if available, is used to calculate online features. From 82de796eb60c0779abfe7c42994620c49aeb39c4 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Thu, 12 Sep 2024 15:01:55 -0700 Subject: [PATCH 08/13] fixes --- articles/ai-services/openai/how-to/provisioned-get-started.md | 2 +- .../openai/how-to/provisioned-throughput-onboarding.md | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/articles/ai-services/openai/how-to/provisioned-get-started.md b/articles/ai-services/openai/how-to/provisioned-get-started.md index 5e67b5b3675..456b8b2b7d3 100644 --- a/articles/ai-services/openai/how-to/provisioned-get-started.md +++ b/articles/ai-services/openai/how-to/provisioned-get-started.md @@ -84,7 +84,7 @@ If you are unsure of the costs, cancel the deployment and proceed once you under * [Understanding the provisioned throughput purchase model](provisioned-throughput-onboarding.md#understanding-the-provisioned-throughput-purchase-model) -:::image type="content" source="../media/provisioned/confirm-pricing.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment." lightbox="../media/provisioned/confirm-pricing.png"::: +:::image type="content" source="../media/provisioned/confirm-pricing.png" alt-text="Screenshot showing the pricing confirmation screen." lightbox="../media/provisioned/confirm-pricing.png"::: If you wish to create your deployment programmatically, you can do so with the following Azure CLI command. Update the `sku-capacity` with the desired number of provisioned throughput units. diff --git a/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md b/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md index c292a432c26..5a35db05e4c 100644 --- a/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md +++ b/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md @@ -81,9 +81,7 @@ Customers that require long-term usage of provisioned deployments, however, migh > It is not recommended to scale production deployments according to incoming traffic and pay for them purely on an hourly basis. There are two reasons for this: > * The cost savings achieved by purchasing an Azure Reservation for Azure OpenAI Provisioned are significant, and it will be less expensive in many cases to maintain a deployment sized for full production volume paid for via a reservation than it would be to scale the deployment with incoming traffic. > * Having unused provisioned quota (PTUs) does not guarentee that capacity will be available to support increasing the size of the deployment when required. Quota limits the maximum number of PTUs that can be deployed, but it is not a capacity guarantee. Provisioned capacity for each region and modal dynamically changes throughout the day and might not be available when required. As a result, it is recommended to maintain a permanant deployment to cover your traffic needs (paid for via a reservation). - -> [!NOTE] -> Charges for deployments on a deleted resource will continue until the resource is purged. To prevent this, delete a resource’s deployment before deleting the resource. For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). +> * Charges for deployments on a deleted resource will continue until the resource is purged. To prevent this, delete a resource’s deployment before deleting the resource. For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). ## Azure Reservations for Azure OpenAI Provisioned   From 102ae21a8a8f6b9048af11308c8ee5a097ff48e9 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Thu, 12 Sep 2024 16:06:53 -0700 Subject: [PATCH 09/13] adding example line --- articles/ai-services/openai/how-to/provisioned-get-started.md | 1 + 1 file changed, 1 insertion(+) diff --git a/articles/ai-services/openai/how-to/provisioned-get-started.md b/articles/ai-services/openai/how-to/provisioned-get-started.md index 456b8b2b7d3..7d557536aed 100644 --- a/articles/ai-services/openai/how-to/provisioned-get-started.md +++ b/articles/ai-services/openai/how-to/provisioned-get-started.md @@ -83,6 +83,7 @@ If you are unsure of the costs, cancel the deployment and proceed once you under * [Azure Pricing Portal](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) * [Understanding the provisioned throughput purchase model](provisioned-throughput-onboarding.md#understanding-the-provisioned-throughput-purchase-model) +The image below shows the pricing confirmation you will see. The price shown is an example only. :::image type="content" source="../media/provisioned/confirm-pricing.png" alt-text="Screenshot showing the pricing confirmation screen." lightbox="../media/provisioned/confirm-pricing.png"::: From e86bc29c74fcd73618aaffa95c11162a367c8882 Mon Sep 17 00:00:00 2001 From: mrbullwinkle <31510320+mrbullwinkle@users.noreply.github.com> Date: Thu, 12 Sep 2024 21:15:44 -0400 Subject: [PATCH 10/13] update --- .../openai/concepts/model-retirements.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/articles/ai-services/openai/concepts/model-retirements.md b/articles/ai-services/openai/concepts/model-retirements.md index 970504416b3..36d9f03d66e 100644 --- a/articles/ai-services/openai/concepts/model-retirements.md +++ b/articles/ai-services/openai/concepts/model-retirements.md @@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI description: Learn about the model deprecations and retirements in Azure OpenAI. ms.service: azure-ai-openai ms.topic: conceptual -ms.date: 09/09/2024 +ms.date: 09/12/2024 ms.custom: manager: nitinme author: mrbullwinkle @@ -91,9 +91,9 @@ These models are currently available for use in Azure OpenAI Service. | Model | Version | Retirement date | Suggested replacements | | ---- | ---- | ---- | --- | -| `gpt-35-turbo` | 0301 | January 27, 2025

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 15, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini` | -| `gpt-35-turbo`
      `gpt-35-turbo-16k` | 0613 | January 27, 2025

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 15, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini`| -| `gpt-35-turbo` | 1106 | No earlier than Nov 17, 2024

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 15, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini` | +| `gpt-35-turbo` | 0301 | January 27, 2025

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 13, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini` | +| `gpt-35-turbo`
      `gpt-35-turbo-16k` | 0613 | January 27, 2025

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 13, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini`| +| `gpt-35-turbo` | 1106 | No earlier than Nov 17, 2024

      Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 13, 2024. | `gpt-35-turbo` (0125)

      `gpt-4o-mini` | | `gpt-35-turbo` | 0125 | No earlier than Feb 22, 2025 | `gpt-4o-mini` | | `gpt-4`
      `gpt-4-32k` | 0314 | June 6, 2025 | `gpt-4o` | | `gpt-4`
      `gpt-4-32k` | 0613 | June 6, 2025 | `gpt-4o` | @@ -115,7 +115,7 @@ These models are currently available for use in Azure OpenAI Service. | Model | Current default version | New default version | Default upgrade date | |---|---|---|---| -| `gpt-35-turbo` | 0301 | 0125 | Deployments of versions `0301`, `0613`, and `1106` set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 15, 2024.| +| `gpt-35-turbo` | 0301 | 0125 | Deployments of versions `0301`, `0613`, and `1106` set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 13, 2024.| @@ -158,6 +158,10 @@ If you're an existing customer looking for information about these models, see [ ## Retirement and deprecation history +## September 12, 2024 + +* `gpt-35-turbo` (0301), (0613), (1106) and `gpt-35-turbo-16k` (0613) auto-update to default upgrade date updated to November 13, 2024. + ## September 9, 2024 * `gpt-35-turbo` (0301) and (0613) retirement changed to January 27, 2025. From d5918577910f4ab93aaa3c2442bafbce05e68a5a Mon Sep 17 00:00:00 2001 From: Zoubaolian <82649822+sally-baolian@users.noreply.github.com> Date: Fri, 13 Sep 2024 12:00:33 +0800 Subject: [PATCH 11/13] Update speech-studio.md --- .../how-to/professional-voice/create-consent/speech-studio.md | 1 - 1 file changed, 1 deletion(-) diff --git a/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md b/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md index e50826e7ad4..c135d9564dc 100644 --- a/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md +++ b/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md @@ -36,7 +36,6 @@ To add a voice talent profile and upload their consent statement, follow these s After the voice talent status is *Succeeded*, you can proceed to [train your custom voice model](../../../../professional-voice-train-voice.md). - ## Next steps > [!div class="nextstepaction"] From e2e5df9bd34c3b5378a0aa08ff1cedc2695e11cd Mon Sep 17 00:00:00 2001 From: Zoubaolian <82649822+sally-baolian@users.noreply.github.com> Date: Fri, 13 Sep 2024 12:03:16 +0800 Subject: [PATCH 12/13] Update speech-studio.md --- .../how-to/professional-voice/create-consent/speech-studio.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md b/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md index c135d9564dc..fc1f9220fde 100644 --- a/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md +++ b/articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/speech-studio.md @@ -27,9 +27,7 @@ To add a voice talent profile and upload their consent statement, follow these s 1. In the **Add new voice talent** wizard, describe the characteristics of the voice you're going to create. The scenarios that you specify here must be consistent with what you provided in the application form. 1. Select **Next**. 1. On the **Upload voice talent statement** page, follow the instructions to upload the voice talent statement you've recorded beforehand. Make sure the verbal statement was [recorded](../../../../record-custom-voice-samples.md) with the same settings, environment, and speaking style as your training data. - - :::image type="content" source="../../../../media/custom-voice/upload-verbal-statement.png" alt-text="Screenshot of the voice talent statement upload dialog."::: - + :::image type="content" source="../../../../media/custom-voice/upload-verbal-statement.png" alt-text="Screenshot of the voice talent statement upload dialog."::: 1. Enter the voice talent name and company name. The voice talent name must be the name of the person who recorded the consent statement. Enter the name in the same language used in the recorded statement. The company name must match the company name that was spoken in the recorded statement. Ensure the company name is entered in the same language as the recorded statement. 1. Select **Next**. 1. Review the voice talent and persona details, and select **Submit**. From be473ffe239886464bcaaeae7712ba3371aadbaa Mon Sep 17 00:00:00 2001 From: Zoubaolian <82649822+sally-baolian@users.noreply.github.com> Date: Fri, 13 Sep 2024 12:04:08 +0800 Subject: [PATCH 13/13] Add files via upload

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