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 4

Commit

468b3cf

1 Parent(s): de15016

fix: create sample data if real data not found in repo

Browse files

Files changed (1) hide show

colab_train_stack29.ipynb +1 -23

colab_train_stack29.ipynb CHANGED Viewed

@@ -100,29 +100,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# STEP 5: Find training data\n",
-    "REPO_DIR = os.path.join(ROOT_DIR, \"stack-2.9\")\n",
-    "DATA_PATH = None\n",
-    "\n",
-    "# Check multiple possible locations\n",
-    "possible_paths = [\n",
-    "    os.path.join(REPO_DIR, \"data/final/train.jsonl\"),\n",
-    "    os.path.join(REPO_DIR, \"training-data/final/train.jsonl\"),\n",
-    "    os.path.join(REPO_DIR, \"data_mini/train_mini.jsonl\"),\n",
-    "]\n",
-    "\n",
-    "for path in possible_paths:\n",
-    "    if os.path.exists(path):\n",
-    "        DATA_PATH = path\n",
-    "        print(f\"✅ Found data at: {path}\")\n",
-    "        break\n",
-    "\n",
-    "if DATA_PATH is None:\n",
-    "    print(\"❌ No training data found!\")\n",
-    "    print(\"\\nSearching for jsonl files:\")\n",
-    "    !find {REPO_DIR} -name \"*.jsonl\" | head -10"
-   ]
   },
   {
    "cell_type": "code",

    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": "# STEP 5: Find or download training data\nREPO_DIR = os.path.join(ROOT_DIR, \"stack-2.9\")\nDATA_PATH = None\n\n# Check multiple possible locations\npossible_paths = [\n    os.path.join(REPO_DIR, \"data/final/train.jsonl\"),\n    os.path.join(REPO_DIR, \"training-data/final/train.jsonl\"),\n    os.path.join(REPO_DIR, \"data_mini/train_mini.jsonl\"),\n]\n\nfor path in possible_paths:\n    if os.path.exists(path):\n        DATA_PATH = path\n        print(f\"✅ Found data at: {path}\")\n        break\n\n# If not found, try to download from a URL or create small sample\nif DATA_PATH is None:\n    print(\"⚠️ Data not found in repo!\")\n    print(\"The training data (data/final/train.jsonl) is not in the GitHub repo.\")\n    print(\"Options:\")\n    print(\"  1. Upload train.jsonl to your Drive at: /content/drive/MyDrive/stack-2.9/data/final/train.jsonl\")\n    print(\"  2. Use a smaller dataset\")\n    \n    # Create minimal sample data for testing (just 100 examples)\n    print(\"\\n📝 Creating minimal sample data (100 examples) for testing...\")\n    sample_data = []\n    sample_prompt = \"\"\"Write a Python function to reverse a string.\n```python\ndef reverse_string(s):\n    return s[::-1]\n```\"\"\"\n    sample_response = \"\"\"Here's the function:\n```python\ndef reverse_string(s):\n    return s[::-1]\n```\nThis uses Python slicing to reverse the string.\"\"\"\n    \n    for i in range(100):\n        sample_data.append({\n            \"messages\": [\n                {\"role\": \"user\", \"content\": sample_prompt},\n                {\"role\": \"assistant\", \"content\": sample_response}\n            ]\n        })\n    \n    # Save sample\n    import json\n    sample_path = os.path.join(REPO_DIR, \"data_mini/sample.jsonl\")\n    os.makedirs(os.path.dirname(sample_path), exist_ok=True)\n    with open(sample_path, 'w') as f:\n        for item in sample_data:\n            f.write(json.dumps(item) + '\\n')\n    \n    DATA_PATH = sample_path\n    print(f\"✅ Created sample data: {DATA_PATH}\")"
   },
   {
    "cell_type": "code",