Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use my-ai-stack/Stack-2-9-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "my-ai-stack/Stack-2-9-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "my-ai-stack/Stack-2-9-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

walidsobhie-code commited on Apr 9

Commit

5ddf5f9

1 Parent(s): 3de42e7

fix: Correct FastMCP.add_tool signature for MCP server

Browse files

Files changed (8) hide show

chat.py → cli/chat.py +0 -0
chat_local.py → cli/chat_local.py +0 -0
chat_simple.py → cli/chat_simple.py +0 -0
enhanced_chat.py → cli/enhanced_chat.py +0 -0
inference_api.py → cli/inference_api.py +0 -0
run_mcp_server.py → cli/run_mcp_server.py +0 -0
web_ui.py → cli/web_ui.py +0 -0
src/mcp_server.py +1 -1

chat.py → cli/chat.py RENAMED Viewed

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chat_local.py → cli/chat_local.py RENAMED Viewed

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chat_simple.py → cli/chat_simple.py RENAMED Viewed

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enhanced_chat.py → cli/enhanced_chat.py RENAMED Viewed

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inference_api.py → cli/inference_api.py RENAMED Viewed

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run_mcp_server.py → cli/run_mcp_server.py RENAMED Viewed

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web_ui.py → cli/web_ui.py RENAMED Viewed

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src/mcp_server.py CHANGED Viewed

@@ -69,7 +69,7 @@ def _register_tool(mcp: FastMCP, tool: BaseTool) -> None:
     async def handler(arguments: dict[str, Any]) -> dict[str, Any]:
         return _call_tool_sync(tool, arguments)
-    mcp.add_tool(tool_name, tool.description, handler, schema)
 def _register_all_tools(mcp: FastMCP) -> int:

     async def handler(arguments: dict[str, Any]) -> dict[str, Any]:
         return _call_tool_sync(tool, arguments)
+    mcp.add_tool(handler, name=tool_name, description=tool.description)
 def _register_all_tools(mcp: FastMCP) -> int: