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 Settings

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
```

Stack-2-9-finetuned / scripts /README_HF.md

walidsobhie-code

feat: Complete Stack 2.9 TUI with benchmarks and pattern management

f80360c 2 months ago

2.66 kB

license: apache-2.0
language:
  - en
  - code
tags:
  - stack-2.9
  - open-source
  - coding-assistant
  - fine-tuned
  - qwen
  - code-generation
library_name: transformers

Stack 2.9 Fine-Tuned Model

A fine-tuned coding assistant model based on {{base_model}}.

Model Details

Property	Value
Base Model	{{base_model}}
Training Data	{{training_examples}} examples
LoRA Rank	{{lora_rank}}
LoRA Alpha	{{lora_alpha}}
Max Context Length	{{max_context_length}}
License	Apache 2.0

Description

Stack 2.9 is a fine-tuned coding assistant model designed for code generation, refactoring, and software development tasks. The model has been fine-tuned on a curated dataset of high-quality code examples and programming tasks.

Training Details

Dataset: {{training_examples}} examples from diverse programming domains
Fine-tuning Method: LoRA (Low-Rank Adaptation)
LoRA Configuration: rank={{lora_rank}}, alpha={{lora_alpha}}
Base Model: {{base_model}}

Benchmarks

Benchmark	Score
HumanEval	{{humaneval_score}}
MBPP	{{mbpp_score}}

Usage

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/stack-2.9-7b"  # Replace with your repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """Write a Python function to calculate the factorial of a number.

```python
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))

Using vLLM for Fast Inference

from vllm import LLM, SamplingParams

llm = LLM(model="your-username/stack-2.9-7b")
sampling_params = SamplingParams(temperature=0.7, max_tokens=200)

prompt = "Write a Python function to reverse a string:"
outputs = llm.generate(prompt, sampling_params)
print(outputs[0].outputs[0].text)

Limitations

The model may generate incorrect code; always verify outputs
Performance may vary across different programming languages
Context window limited to {{max_context_length}} tokens

License

This model is licensed under the Apache 2.0 license.

Citation

If you use this model in your research, please cite:

@misc{stack-2.9,
  author = {Stack Team},
  title = {Stack 2.9: Fine-tuned Coding Assistant},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/your-username/stack-2.9-7b}
}

Model uploaded via upload_hf.py script