Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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 Settings
- 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
File size: 2,102 Bytes
fcb2b04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | 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 |