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
```

Stack-2-9-finetuned / stack /voice /docker-compose.yml

walidsobhie-code

refactor: Squeeze folders further - cleaner structure

65888d5 about 2 months ago

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2.1 kB

	version: '3.8'

	services:
	voice-api:
	build: .
	ports:
	- "8000:8000"
	volumes:
	- ./voice_models:/app/voice_models
	- ./audio_files:/app/audio_files
	environment:
	- MODEL_PATH=/app/models/coqui_xtts
	- VOICE_CACHE_DIR=/app/voice_cache
	- WORKERS=4
	deploy:
	resources:
	limits:
	cpus: '2.0'
	memory: 4G
	reservations:
	cpus: '1.0'
	memory: 2G
	restart: unless-stopped

	tts-model:
	image: coqui/tts:latest
	ports:
	- "9000:9000"
	volumes:
	- ./models:/models
	- ./tts_cache:/tts_cache
	environment:
	- MODEL_NAME=x TTS
	- MODEL_PATH=/models/coqui_xtts
	- CACHE_DIR=/tts_cache
	- GPU_SUPPORT=${GPU_SUPPORT:-false}
	deploy:
	resources:
	limits:
	cpus: '4.0'
	memory: 8G
	${GPU_LIMITS}
	reservations:
	cpus: '2.0'
	memory: 4G
	restart: unless-stopped

	redis:
	image: redis:alpine
	ports:
	- "6379:6379"
	volumes:
	- ./redis_data:/data
	command: redis-server --appendonly yes
	deploy:
	resources:
	limits:
	cpus: '0.5'
	memory: 256M
	reservations:
	cpus: '0.25'
	memory: 128M
	restart: unless-stopped

	# Optional: Speech-to-text service for voice input
	stt-service:
	image: vosk/kaldi:latest
	ports:
	- "9001:9001"
	volumes:
	- ./models/vosk:/models/vosk
	environment:
	- MODEL_PATH=/models/vosk/model
	deploy:
	resources:
	limits:
	cpus: '2.0'
	memory: 4G
	reservations:
	cpus: '1.0'
	memory: 2G
	restart: unless-stopped

	volumes:
	voice_models:
	driver: local
	audio_files:
	driver: local
	models:
	driver: local
	tts_cache:
	driver: local
	redis_data:
	driver: local
	vosk_models:
	driver: local

	networks:
	default:
	driver: bridge

	# Environment variables for GPU support
	# Set GPU_SUPPORT=true and provide GPU_LIMITS when using GPU
	# Example: GPU_LIMITS=nvidia.com/gpu=1