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
```

walidsobhie-code Claude Opus 4.6 commited on Apr 4

Commit

8f0e2c5

1 Parent(s): fb43392

fix: change to /kaggle/working before cloning to avoid directory error

Browse files

Files changed (1) hide show

kaggle_train_stack29.ipynb +1 -1

kaggle_train_stack29.ipynb CHANGED Viewed

@@ -40,7 +40,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# STEP 2: Clone repo and setup paths\nimport os\nimport shutil\nimport subprocess\n\nREPO_DIR = \"/kaggle/working/stack-2.9\"\nMODEL_DIR = os.path.join(REPO_DIR, \"base_model_qwen7b\")\nOUTPUT_DIR = os.path.join(REPO_DIR, \"training_output\")\n\n# Remove old repo if exists\nif os.path.exists(REPO_DIR):\n    shutil.rmtree(REPO_DIR)\n\n# Clone fresh\nsubprocess.run([\"git\", \"clone\", \"https://github.com/my-ai-stack/stack-2.9.git\", REPO_DIR], check=True)\nos.chdir(REPO_DIR)\n\nprint(f\"✅ Working in: {os.getcwd()}\")\nprint(f\"   MODEL_DIR: {MODEL_DIR}\")\nprint(f\"   OUTPUT_DIR: {OUTPUT_DIR}\")"
   },
   {
    "cell_type": "code",

    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": "# STEP 2: Clone repo and setup paths\nimport os\nimport shutil\nimport subprocess\n\n# Change to a valid directory first (in case we're in a deleted folder)\nos.chdir(\"/kaggle/working\")\n\nREPO_DIR = \"/kaggle/working/stack-2.9\"\nMODEL_DIR = os.path.join(REPO_DIR, \"base_model_qwen7b\")\nOUTPUT_DIR = os.path.join(REPO_DIR, \"training_output\")\n\n# Remove old repo if exists (force fresh clone)\nif os.path.exists(REPO_DIR):\n    shutil.rmtree(REPO_DIR)\n\n# Clone fresh (now includes the input_path fix)\nsubprocess.run([\"git\", \"clone\", \"https://github.com/my-ai-stack/stack-2.9.git\", REPO_DIR], check=True)\nos.chdir(REPO_DIR)\n\nprint(f\"✅ Working in: {os.getcwd()}\")\nprint(f\"   MODEL_DIR: {MODEL_DIR}\")\nprint(f\"   OUTPUT_DIR: {OUTPUT_DIR}\")"
   },
   {
    "cell_type": "code",