Using Gemma on GTX 1060

src/continue-dev/config.json

@@ -5,6 +5,12 @@
         "provider": "ollama",
         "model": "mistral:latest",
         "apiBase": "http://localhost:11434/"
+      },
+      {
+        "title": "Gemma2B",
+        "provider": "ollama",
+        "model": "gemma:2b",
+        "apiBase": "http://localhost:11434/"
       }
     ],
     "slashCommands": [
@@ -63,7 +69,7 @@
       "provider": "ollama",
       "model": "mistral:latest"
     },
-    "allowAnonymousTelemetry": true,
+    "allowAnonymousTelemetry": false,
     "embeddingsProvider": {
       "provider": "transformers.js"
     }

src/ollama/bhoomi/json_output.py

@@ -0,0 +1,41 @@
+import json
+
+from langchain_community.chat_models import ChatOllama
+from langchain_core.messages import HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+json_schema = {
+    "title": "Person",
+    "description": "Identifying information about a person.",
+    "type": "object",
+    "properties": {
+        "name": {"title": "Name", "description": "The person's name", "type": "string"},
+        "age": {"title": "Age", "description": "The person's age", "type": "integer"},
+        "fav_food": {
+            "title": "Fav Food",
+            "description": "The person's favorite food",
+            "type": "string",
+        },
+    },
+    "required": ["name", "age"],
+}
+
+llm = ChatOllama(model="gemma:2b")
+
+messages = [
+    HumanMessage(
+        content="Please tell me about a person using the following JSON schema:"
+    ),
+    HumanMessage(content="{dumps}"),
+    HumanMessage(
+        content="Now, considering the schema, tell me about a person named Sachin who is 32 years old and loves Biryani."
+    ),
+]
+
+prompt = ChatPromptTemplate.from_messages(messages)
+dumps = json.dumps(json_schema, indent=2)
+
+chain = prompt | llm | StrOutputParser()
+
+print(chain.invoke({"dumps": dumps}))

src/ollama/bhoomi/main.py

@@ -2,14 +2,19 @@
 
 from langchain_community.llms import Ollama
 
-llm = Ollama(model="mistral")
+llm = Ollama(model="gemma:2b")
 
 # test 1
 
 # llm.invoke("Tell me a joke")
 
 # test 2
-query = "Tell me a joke"
+query = "What are the top 3 important facilities required for a Robot to survive on Mars?"
 
-for chunks in llm.stream(query):
+query2 = "What top 5 Sensors are necessary for Robots on Mars?"
+
+query3 = "Write a python program to connect the 5 sensors"
+
+
+for chunks in llm.stream(query2):
     print(chunks)