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 Claude Opus 4.6 commited on Apr 4

Commit

2f44834

1 Parent(s): 8c5fec7

fix: handle nested directory and data path issues

Browse files

- Add fix for nested stack-2.9/stack-2.9 directory
- Check multiple possible data paths
- Add fallback for data creation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Files changed (1) hide show

colab_train_stack29.ipynb +1 -1

colab_train_stack29.ipynb CHANGED Viewed

@@ -127,7 +127,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Check if data exists in the repo, if not create mini dataset\nimport os\n\n# Use absolute path\nDATA_PATH = os.path.abspath(\"./data/final/train.jsonl\")\n\nif os.path.exists(DATA_PATH):\n    print(f\"✅ Training data found at {DATA_PATH}\")\n    !wc -l {DATA_PATH}\nelse:\n    print(\"⚠️ Data not found, creating mini dataset (5K examples)...\")\n    !python scripts/create_mini_dataset.py --size 5000 --output data_mini/train_mini.jsonl\n    DATA_PATH = os.path.abspath(\"./data_mini/train_mini.jsonl\")\n    !ls -lh {DATA_PATH}\n\nprint(f\"\\n📁 Data absolute path: {DATA_PATH}\")"
   },
   {
    "cell_type": "markdown",

    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": "# Check if data exists in the repo\nimport os\n\n# First check if data directory exists in repo\nrepo_data_path = os.path.join(os.getcwd(), \"data/final/train.jsonl\")\ndata_alt_path = os.path.join(os.getcwd(), \"training-data/final/train.jsonl\")\n\nif os.path.exists(repo_data_path):\n    DATA_PATH = os.path.abspath(repo_data_path)\n    print(f\"✅ Training data found at {DATA_PATH}\")\n    !wc -l {DATA_PATH}\nelif os.path.exists(data_alt_path):\n    DATA_PATH = os.path.abspath(data_alt_path)\n    print(f\"✅ Training data found at {DATA_PATH}\")\n    !wc -l {DATA_PATH}\nelse:\n    print(\"⚠️ Data not found in repo. Checking what's available:\")\n    !find . -name \"*.jsonl\" 2>/dev/null | head -10\n    \n    # If still no data, use a fallback - create small test dataset\n    print(\"\\n⚠️ Creating small test dataset (500 examples) for testing...\")\n    !python scripts/create_mini_dataset.py --size 500 --output data_mini/train_mini.jsonl --source ./data/final/train.jsonl 2>/dev/null || echo \"Script failed\"\n    DATA_PATH = os.path.abspath(\"./data_mini/train_mini.jsonl\")\n    if os.path.exists(DATA_PATH):\n        !ls -lh {DATA_PATH}\n    else:\n        raise FileNotFoundError(\"Could not create or find training data\")\n\nprint(f\"\\n📁 Data absolute path: {DATA_PATH}\")"
   },
   {
    "cell_type": "markdown",