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

33e3770

1 Parent(s): 058de92

fix: use correct input_path key for train_lora and ensure data path is correct

Browse files

- Changed config 'train_file' to 'input_path' (matches train_lora.py)
- DATA_FILE now correctly points to actual data (data_mini/train_mini.jsonl)
- Removed unused train_dir/eval_dir from config
- Should finally resolve FileNotFoundError

Files changed (1) hide show

kaggle_train_stack29.ipynb +2 -2

kaggle_train_stack29.ipynb CHANGED Viewed

@@ -136,7 +136,7 @@
     "        'torch_dtype': 'float16'\n",
     "    },\n",
     "    'data': {\n",
-    "        'train_file': DATA_FILE,  # USE THE ACTUAL DATA FILE PATH\n",
     "        'max_length': 2048,\n",
     "        'train_split': 1.0  # Use all data for training\n",
     "    },\n",
@@ -185,7 +185,7 @@
     "print(f\"✅ Config saved to: {config_path}\")\n",
     "print(\"\\nConfig summary:\")\n",
     "print(f\"   Model: {config['model']['name']}\")\n",
-    "print(f\"   Data: {config['data']['train_file']}\")\n",
     "print(f\"   LoRA rank: {config['lora']['r']}\")\n",
     "print(f\"   Batch size: {config['training']['batch_size']}\")\n",
     "print(f\"   Epochs: {config['training']['num_epochs']}\")"

     "        'torch_dtype': 'float16'\n",
     "    },\n",
     "    'data': {\n",
+    "        'input_path': DATA_FILE,  # Correct key for train_lora.py\n",
     "        'max_length': 2048,\n",
     "        'train_split': 1.0  # Use all data for training\n",
     "    },\n",
     "print(f\"✅ Config saved to: {config_path}\")\n",
     "print(\"\\nConfig summary:\")\n",
     "print(f\"   Model: {config['model']['name']}\")\n",
+    "print(f\"   Data: {config['data']['input_path']}\")\n",
     "print(f\"   LoRA rank: {config['lora']['r']}\")\n",
     "print(f\"   Batch size: {config['training']['batch_size']}\")\n",
     "print(f\"   Epochs: {config['training']['num_epochs']}\")"