docs: add github provider to docs

docs/my-website/docs/completion/input.md

@@ -48,10 +48,10 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea
 |Anyscale | ✅ | ✅ | ✅ | ✅ | ✅ |
 |Cohere| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |   |   |
 |Huggingface| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |   |    |
-|Openrouter| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | | | |
+|Openrouter| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ |✅ | | | |
 |AI21| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |
 |VertexAI| ✅ | ✅ |  | ✅ | ✅ |  |  |  |  |   | | | | ✅ | ✅ | | |
-|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   | | | | | ✅ (for anthropic) | |
+|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   | | | | | ✅ (model dependent) | |
 |Sagemaker| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |
 |TogetherAI| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   | ✅ |
 |AlephAlpha| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |   |  |   |
@@ -61,6 +61,7 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea
 |Ollama| ✅ | ✅ | ✅ | ✅ | ✅ |  |   | ✅ |  |   | | | ✅ |  | |✅| | | | | | |
 |Databricks| ✅ | ✅ | ✅ | ✅ | ✅ |  |   | |  |   | | | | | |
 |ClarifAI| ✅ | ✅ | |✅ | ✅ |  |   | |  |   | | | | | |
+|Github| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ |✅ (model dependent)|✅ (model dependent)| | |
 :::note
 
 By default, LiteLLM raises an exception if the openai param being passed in isn't supported. 

docs/my-website/docs/providers/github.md

@@ -0,0 +1,261 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 🆕 Github
+https://github.com/marketplace/models
+
+:::tip
+
+**We support ALL Github models, just set `model=groq/<any-model-on-github>` as a prefix when sending litellm requests**
+
+:::
+
+## API Key
+```python
+# env variable
+os.environ['GITHUB_API_KEY']
+```
+
+## Sample Usage
+```python
+from litellm import completion
+import os
+
+os.environ['GITHUB_API_KEY'] = ""
+response = completion(
+    model="github/llama3-8b-8192", 
+    messages=[
+       {"role": "user", "content": "hello from litellm"}
+   ],
+)
+print(response)
+```
+
+## Sample Usage - Streaming
+```python
+from litellm import completion
+import os
+
+os.environ['GITHUB_API_KEY'] = ""
+response = completion(
+    model="github/llama3-8b-8192", 
+    messages=[
+       {"role": "user", "content": "hello from litellm"}
+   ],
+    stream=True
+)
+
+for chunk in response:
+    print(chunk)
+```
+
+
+
+## Usage with LiteLLM Proxy 
+
+### 1. Set Github Models on config.yaml
+
+```yaml
+model_list:
+  - model_name: github-llama3-8b-8192 # Model Alias to use for requests
+    litellm_params:
+      model: github/llama3-8b-8192
+      api_key: "os.environ/GITHUB_API_KEY" # ensure you have `GITHUB_API_KEY` in your .env
+```
+
+### 2. Start Proxy 
+
+```
+litellm --config config.yaml
+```
+
+### 3. Test it
+
+Make request to litellm proxy
+
+<Tabs>
+<TabItem value="Curl" label="Curl Request">
+
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--data ' {
+      "model": "github-llama3-8b-8192",
+      "messages": [
+        {
+          "role": "user",
+          "content": "what llm are you"
+        }
+      ]
+    }
+'
+```
+</TabItem>
+<TabItem value="openai" label="OpenAI v1.0.0+">
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+response = client.chat.completions.create(model="github-llama3-8b-8192", messages = [
+    {
+        "role": "user",
+        "content": "this is a test request, write a short poem"
+    }
+])
+
+print(response)
+
+```
+</TabItem>
+<TabItem value="langchain" label="Langchain">
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
+from langchain.schema import HumanMessage, SystemMessage
+
+chat = ChatOpenAI(
+    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
+    model = "github-llama3-8b-8192",
+    temperature=0.1
+)
+
+messages = [
+    SystemMessage(
+        content="You are a helpful assistant that im using to make a test request to."
+    ),
+    HumanMessage(
+        content="test from litellm. tell me why it's amazing in 1 sentence"
+    ),
+]
+response = chat(messages)
+
+print(response)
+```
+</TabItem>
+</Tabs>
+
+
+
+## Supported Models - ALL Github Models Supported!
+We support ALL Github models, just set `github/` as a prefix when sending completion requests
+
+| Model Name         | Usage                                           |
+|--------------------|---------------------------------------------------------|
+| llama-3.1-8b-instant     | `completion(model="github/llama-3.1-8b-instant", messages)`     | 
+| llama-3.1-70b-versatile    | `completion(model="github/llama-3.1-70b-versatile", messages)`    | 
+| llama-3.1-405b-reasoning    | `completion(model="github/llama-3.1-405b-reasoning", messages)`    | 
+| llama3-8b-8192     | `completion(model="github/llama3-8b-8192", messages)`     | 
+| llama3-70b-8192    | `completion(model="github/llama3-70b-8192", messages)`    | 
+| llama2-70b-4096    | `completion(model="github/llama2-70b-4096", messages)`    | 
+| mixtral-8x7b-32768 | `completion(model="github/mixtral-8x7b-32768", messages)` |
+| gemma-7b-it        | `completion(model="github/gemma-7b-it", messages)`        |  
+
+## Github - Tool / Function Calling Example
+
+```python
+# Example dummy function hard coded to return the current weather
+import json
+def get_current_weather(location, unit="fahrenheit"):
+    """Get the current weather in a given location"""
+    if "tokyo" in location.lower():
+        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
+    elif "san francisco" in location.lower():
+        return json.dumps(
+            {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}
+        )
+    elif "paris" in location.lower():
+        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+
+
+
+# Step 1: send the conversation and available functions to the model
+messages = [
+    {
+        "role": "system",
+        "content": "You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.",
+    },
+    {
+        "role": "user",
+        "content": "What's the weather like in San Francisco?",
+    },
+]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                    },
+                },
+                "required": ["location"],
+            },
+        },
+    }
+]
+response = litellm.completion(
+    model="github/llama3-8b-8192",
+    messages=messages,
+    tools=tools,
+    tool_choice="auto",  # auto is default, but we'll be explicit
+)
+print("Response\n", response)
+response_message = response.choices[0].message
+tool_calls = response_message.tool_calls
+
+
+# Step 2: check if the model wanted to call a function
+if tool_calls:
+    # Step 3: call the function
+    # Note: the JSON response may not always be valid; be sure to handle errors
+    available_functions = {
+        "get_current_weather": get_current_weather,
+    }
+    messages.append(
+        response_message
+    )  # extend conversation with assistant's reply
+    print("Response message\n", response_message)
+    # Step 4: send the info for each function call and function response to the model
+    for tool_call in tool_calls:
+        function_name = tool_call.function.name
+        function_to_call = available_functions[function_name]
+        function_args = json.loads(tool_call.function.arguments)
+        function_response = function_to_call(
+            location=function_args.get("location"),
+            unit=function_args.get("unit"),
+        )
+        messages.append(
+            {
+                "tool_call_id": tool_call.id,
+                "role": "tool",
+                "name": function_name,
+                "content": function_response,
+            }
+        )  # extend conversation with function response
+    print(f"messages: {messages}")
+    second_response = litellm.completion(
+        model="github/llama3-8b-8192", messages=messages
+    )  # get a new response from the model where it can see the function response
+    print("second response\n", second_response)
+```

docs/my-website/sidebars.js

@@ -161,6 +161,7 @@ const sidebars = {
         "providers/perplexity", 
         "providers/friendliai",
         "providers/groq", 
+        "providers/github", 
         "providers/deepseek", 
         "providers/fireworks_ai",
         "providers/clarifai",