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 2

Commit

3bc915b

1 Parent(s): 2088481

Update README: add requested badges (Apache 2.0, OpenRouter, Hugging Face, HumanEval, MBPP) and acknowledge sub-agent enhancements

Browse files

Files changed (1) hide show

README.md +7 -1

README.md CHANGED Viewed

@@ -1,6 +1,10 @@
 <p align="center">
   <img src="https://img.shields.io/github/stars/my-ai-stack/stack-2.9" alt="Stars">
-  <img src="https://img.shields.io/github/license/my-ai-stack-stack-2.9" alt="License">
   <img src="https://img.shields.io/python version/3.10+-blue" alt="Python">
   <img src="https://img.shields.io/discord" alt="Discord">
 </p>
@@ -44,6 +48,8 @@ These scores were therefore **unverifiable** and potentially misleading.
 We are rebuilding the evaluation infrastructure with proper methodology:
 1. **Official datasets**: HumanEval (164 problems), MBPP (500 problems)
 2. **Reproducible runs**: Full logs, config files, and per-problem results
 3. **Standard metrics**: Pass@1 with confidence intervals, using k≥100 samples

 <p align="center">
   <img src="https://img.shields.io/github/stars/my-ai-stack/stack-2.9" alt="Stars">
+  <img src="https://img.shields.io/github/license/my-ai-stack/stack-2.9?logo=apache" alt="License: Apache 2.0">
+  <img src="https://img.shields.io/badge/OpenRouter-Supported-green?logo=openrouter" alt="OpenRouter">
+  <img src="https://img.shields.io/badge/Hugging%20Face-Model-green?logo=huggingface" alt="Hugging Face">
+  <img src="https://img.shields.io/badge/HumanEval-Evaluation%20In%20Progress-yellow?logo=python" alt="HumanEval">
+  <img src="https://img.shields.io/badge/MBPP-Evaluation%20In%20Progress-yellow?logo=python" alt="MBPP">
   <img src="https://img.shields.io/python version/3.10+-blue" alt="Python">
   <img src="https://img.shields.io/discord" alt="Discord">
 </p>
 We are rebuilding the evaluation infrastructure with proper methodology:
+**🔬 Recent Enhancement**: This release's documentation improvements, OpenRouter integration, tool system documentation, and evaluation audit were performed by autonomous sub-agents. See [EVALUATION.md](EVALUATION.md) for details.
 1. **Official datasets**: HumanEval (164 problems), MBPP (500 problems)
 2. **Reproducible runs**: Full logs, config files, and per-problem results
 3. **Standard metrics**: Pass@1 with confidence intervals, using k≥100 samples