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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
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

Commit History

Apr 9

21:14
reorganize: consolidate root level to 20 folders⁠

walidsobhie-code +1: Claude Opus 4.6

b8e3e42
19:33
chore: Rename MCP server to Stack2.9

walidsobhie-code

c7f1596

Apr 5

23:59
feat: add code completion generator and model registry tools⁠

walidsobhie-code

4ca507e
23:37
feat: add production infrastructure - CI/CD, Docker, code quality, and monitoring⁠

walidsobhie-code

b5998ff
23:07
feat: add evaluation scripts, tool calling data generator, and 7B training configs⁠

walidsobhie-code

183b3b6

Apr 4

19:16
feat: add RTMP tool extraction scripts⁠

walidsobhie-code +1: Claude Opus 4.6

30d572f
00:34
fix: correct data path from training-data/final to data/final⁠

walidsobhie-code +1: Claude Opus 4.6

12c2955

Apr 3

23:45
fix: syntax errors in notebook and create_mini_dataset script⁠

walidsobhie-code +1: Claude Opus 4.6

2c32ffd
19:22
docs: Add official launch plan⁠

walidsobhie-code +1: Claude Opus 4.6

d083607
17:30
refactor: Clean up project structure - fewer root folders⁠

walidsobhie-code +1: Claude Opus 4.6

99a7be2
17:01
feat: add TypeScript modules and reorganize project structure⁠

walidsobhie-code +1: Claude Opus 4.6

bfc7d04

Apr 2

19:40
feat: zero-cost Colab training pipeline and proper evaluation⁠

walidsobhie-code

49ffe54
19:24
feat: Together AI integration complete⁠

walidsobhie-code

0908455

Apr 1

22:48
feat: Complete Stack 2.9 TUI with benchmarks and pattern management⁠

walidsobhie-code +1: Claude Opus 4.6

f80360c
15:51
Add training scripts, eval tools, docs, and scaled datasets⁠

walidsobhie-code

6379283

Commit History

Apr 9

reorganize: consolidate root level to 20 folders⁠

chore: Rename MCP server to Stack2.9

Apr 5

feat: add code completion generator and model registry tools⁠

feat: add production infrastructure - CI/CD, Docker, code quality, and monitoring⁠

feat: add evaluation scripts, tool calling data generator, and 7B training configs⁠

Apr 4

feat: add RTMP tool extraction scripts⁠

fix: correct data path from training-data/final to data/final⁠

Apr 3

fix: syntax errors in notebook and create_mini_dataset script⁠

docs: Add official launch plan⁠

refactor: Clean up project structure - fewer root folders⁠

feat: add TypeScript modules and reorganize project structure⁠

Apr 2

feat: zero-cost Colab training pipeline and proper evaluation⁠

feat: Together AI integration complete⁠

Apr 1

feat: Complete Stack 2.9 TUI with benchmarks and pattern management⁠

Add training scripts, eval tools, docs, and scaled datasets⁠