Merge pull request #75 from lmstudio-ai/yagil/catalog-update-032624

Add Stable Code Instruct 3B and Hermes 2 Pro Mistral 7B

models/Hermes-2-Pro-Mistral-7B.json

@@ -0,0 +1,41 @@
+{
+  "_descriptorVersion": "0.0.1",  
+  "datePublished": "2024-03-12T06:52:19.000Z",
+  "name": "Hermes 2 Pro Mistral 7B",
+  "description": "Hermes 2 Pro, an updated version of Nous Hermes 2, incorporates an enhanced and cleaned OpenHermes 2.5 Dataset alongside a new in-house developed dataset for Function Calling and JSON Mode. This version retains its robust performance in general tasks and conversations while showing notable improvements in Function Calling, JSON Structured Outputs, achieving a 90% score in function calling evaluation conducted with Fireworks.AI, and 84% in structured JSON Output evaluation. It introduces a special system prompt and a multi-turn function calling structure, incorporating a chatml role to streamline and simplify function calling.",
+  "author": {
+    "name": "NousResearch",
+    "url": "https://twitter.com/NousResearch",
+    "blurb": "We are dedicated to advancing the field of natural language processing, in collaboration with the open-source community, through bleeding-edge research and a commitment to symbiotic development."
+  },
+  "numParameters": "7B",
+  "resources": {
+    "canonicalUrl": "https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B",
+    "downloadUrl": "https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"
+  },
+  "trainedFor": "chat",
+  "arch": "mistral",
+  "files": {
+    "highlighted": {
+      "economical": {
+        "name": "Hermes-2-Pro-Mistral-7B.Q4_0.gguf"
+      }
+    },
+    "all": [
+      {
+        "name": "Hermes-2-Pro-Mistral-7B.Q4_0.gguf",
+        "url": "https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_0.gguf",
+        "sizeBytes": 4109098752,
+        "quantization": "q4_0",
+        "format": "gguf",
+        "sha256checksum": "f446c3125026f7af6757dd097dda02280adc85e908c058bd6f1c41a118354745",
+        "publisher": {
+          "name": "NousResearch",
+          "socialUrl": "https://twitter.com/NousResearch"
+        },
+        "respository": "NousResearch/Hermes-2-Pro-Mistral-7B-GGUF",
+        "repositoryUrl": "https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF"
+      }
+    ]
+  }
+}

models/google-gemma-2b.json

@@ -2,7 +2,7 @@
     "_descriptorVersion": "0.0.1",  
     "datePublished": "2024-02-21T16:54:57.000Z",
     "name": "Google's Gemma 2B Instruct",
-    "description": "** Requires LM Studio 0.2.15 or newer ** Gemma is a family of lightweight LLMs built from the same research and technology Google used to create the Gemini models. Gemma models are available in two sizes, 2 billion and 7 billion parameters. These models are trained on up to 6T tokens of primarily English web documents, mathematics, and code, using a transformer architecture with enhancements like Multi-Query Attention, RoPE Embeddings, GeGLU Activations, and advanced normalization techniques.",
+    "description": "Gemma is a family of lightweight LLMs built from the same research and technology Google used to create the Gemini models. Gemma models are available in two sizes, 2 billion and 7 billion parameters. These models are trained on up to 6T tokens of primarily English web documents, mathematics, and code, using a transformer architecture with enhancements like Multi-Query Attention, RoPE Embeddings, GeGLU Activations, and advanced normalization techniques.",
     "author": {
       "name": "Google DeepMind",
       "url": "https://deepmind.google",

models/stable-code-instruct-3b.json

@@ -0,0 +1,42 @@
+{
+  "_descriptorVersion": "0.0.1",  
+  "datePublished": "2024-03-20T00:31:49.000Z",
+  "name": "Stable Code Instruct 3B ",
+  "description": "Stable Code Instruct 3B is a decoder-only language model with 2.7 billion parameters, developed from the stable-code-3b. It has been trained on a combination of publicly available and synthetic datasets, with the latter generated through Direct Preference Optimization (DPO). This model has shown competitive performance in comparison to other models of similar size, as evidenced by its results on the MultiPL-E metrics across various programming languages using the BigCode Evaluation Harness, and on code-related tasks in MT Bench. It is fine-tuned for use in general code/software engineering conversations and SQL query generation and discussion.",
+  "author": {
+    "name": "Stability AI",
+    "url": "https://stability.ai/",
+    "blurb": "Stability AI is developing cutting-edge open AI models for Image, Language, Audio, Video, 3D and Biology."
+  },
+  "numParameters": "3B",
+  "resources": {
+    "canonicalUrl": "https://huggingface.co/stabilityai/stable-code-instruct-3b",
+    "downloadUrl": "https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF",
+    "paperUrl": "https://drive.google.com/file/d/16-DGsR5-qwoPztZ6HcM7KSRUxIXrjlSm/view"
+  },
+  "trainedFor": "instruct",
+  "arch": "stablelm",
+  "files": {
+    "highlighted": {
+      "most_capable": {
+        "name": "stable-code-instruct-3b-Q8_0.gguf"
+      }
+    },
+    "all": [
+      {
+        "name": "stable-code-instruct-3b-Q8_0.gguf",
+        "url": "https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/resolve/main/stable-code-instruct-3b-Q8_0.gguf",
+        "sizeBytes": 2972926176,
+        "quantization": "Q8_0",
+        "format": "gguf",
+        "sha256checksum": "2ffc06aacad9b90fe633c3920d3784618d7419e5704151e9ab7087a5958a3c63",
+        "publisher": {
+          "name": "Bartowski",
+          "socialUrl": "https://huggingface.co/bartowski"
+        },
+        "respository": "bartowski/stable-code-instruct-3b-GGUF",
+        "repositoryUrl": "https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF"
+      }
+    ]
+  }
+}

schema.json

@@ -59,7 +59,7 @@
       },
       "arch": {
         "type": "string",
-        "enum": ["llama", "pythia", "gpt-neo-x", "gpt-j", "mpt", "replit", "starcoder", "falcon", "mistral", "stablelm", "phi2", "qwen2", "gemma"]
+        "enum": ["llama", "pythia", "gpt-neo-x", "gpt-j", "mpt", "replit", "starcoder", "falcon", "mistral", "stablelm", "phi2", "qwen2", "gemma", "command-r"]
       },
       "description": {
         "type": "string"