Instructions to use GhostScientist/qwen25-coder-1.5b-codealpaca-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhostScientist/qwen25-coder-1.5b-codealpaca-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GhostScientist/qwen25-coder-1.5b-codealpaca-sft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GhostScientist/qwen25-coder-1.5b-codealpaca-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GhostScientist/qwen25-coder-1.5b-codealpaca-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GhostScientist/qwen25-coder-1.5b-codealpaca-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GhostScientist/qwen25-coder-1.5b-codealpaca-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GhostScientist/qwen25-coder-1.5b-codealpaca-sft
- SGLang
How to use GhostScientist/qwen25-coder-1.5b-codealpaca-sft 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 "GhostScientist/qwen25-coder-1.5b-codealpaca-sft" \ --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": "GhostScientist/qwen25-coder-1.5b-codealpaca-sft", "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 "GhostScientist/qwen25-coder-1.5b-codealpaca-sft" \ --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": "GhostScientist/qwen25-coder-1.5b-codealpaca-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use GhostScientist/qwen25-coder-1.5b-codealpaca-sft with Docker Model Runner:
docker model run hf.co/GhostScientist/qwen25-coder-1.5b-codealpaca-sft
Commit History
-
Upload README.md with huggingface_hub
Verified signature9c47431
-
Training in progress, step 3381
Verified signaturef506484 -
Training in progress, step 3200
Verified signature2e23d8d -
Training in progress, step 3000
Verified signature059af4e -
Training in progress, step 2800
Verified signature4ec8b9f -
Training in progress, step 2600
Verified signaturea84f245 -
Training in progress, step 2400
Verified signature73a9bdb -
Training in progress, step 2200
Verified signature6f3ef51 -
Training in progress, step 2000
Verified signature8953e26 -
Training in progress, step 1800
Verified signaturea116ca0 -
Training in progress, step 1600
Verified signature3fb0a43 -
Training in progress, step 1400
Verified signature9e4ea9c -
Training in progress, step 1200
Verified signaturef974292 -
Training in progress, step 1000
Verified signatureb15b4dc -
Training in progress, step 800
Verified signaturee3ad797 -
Training in progress, step 600
Verified signature3665714 -
Training in progress, step 400
Verified signature8156dca -
Training in progress, step 200
Verified signaturee572a52 -
initial commit
Verified signature584af57