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 5

Commit

65c52b2

1 Parent(s): f4b31b2

feat: add Google Drive auto-save to prevent losing model outputs

Browse files

- Mount Google Drive after cloning repo
- Auto-copy merged model to Drive after merge completes
- Output saved to MyDrive/stack-2.9-output/
- Prevents losing model when Kaggle session expires

Files changed (1) hide show

kaggle_train_stack29_v5.ipynb +29 -1

kaggle_train_stack29_v5.ipynb CHANGED Viewed

@@ -46,6 +46,23 @@
     "print('\u2705 Repo ready:', REPO_DIR)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -194,7 +211,18 @@
     "\n",
     "print('\\n\u2705 Merge complete!')\n",
     "print(f'Merged model: {merged_dir}')\n",
-    "!ls -lh {merged_dir}"
    ]
   },
   {

     "print('\u2705 Repo ready:', REPO_DIR)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mount Google Drive to save outputs permanently\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "\n",
+    "# Create permanent output directory in Google Drive\n",
+    "DRIVE_OUTPUT_DIR = '/content/drive/MyDrive/stack-2.9-output'\n",
+    "os.makedirs(DRIVE_OUTPUT_DIR, exist_ok=True)\n",
+    "\n",
+    "print(f\"\u2705 Google Drive mounted: {DRIVE_OUTPUT_DIR}\")\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
     "\n",
     "print('\\n\u2705 Merge complete!')\n",
     "print(f'Merged model: {merged_dir}')\n",
+    "!ls -lh {merged_dir}\n",
+    "\n",
+    "# Copy merged model to Google Drive\n",
+    "import shutil\n",
+    "drive_merge_dir = os.path.join(DRIVE_OUTPUT_DIR, 'merged')\n",
+    "if os.path.exists(merged_dir):\n",
+    "    if os.path.exists(drive_merge_dir):\n",
+    "        shutil.rmtree(drive_merge_dir)\n",
+    "    shutil.copytree(merged_dir, drive_merge_dir)\n",
+    "    print(f\"\u2705 Model copied to Google Drive: {drive_merge_dir}\")\n",
+    "else:\n",
+    "    print(\"\u26a0\ufe0f Merged model not found, skipping Drive copy\")\n"
    ]
   },
   {