Instructions to use TechxGenus/Seed-Coder-8B-Instruct-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TechxGenus/Seed-Coder-8B-Instruct-DWQ with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("TechxGenus/Seed-Coder-8B-Instruct-DWQ") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use TechxGenus/Seed-Coder-8B-Instruct-DWQ with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "TechxGenus/Seed-Coder-8B-Instruct-DWQ"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TechxGenus/Seed-Coder-8B-Instruct-DWQ" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TechxGenus/Seed-Coder-8B-Instruct-DWQ", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 7489b153f60c2f35fd5538fa0119324a7feefec46da4e5d0ad6f5bcf260bb3d3
- Size of remote file:
- 4.64 GB
- SHA256:
- fd0defa4d1f285677b8d5b06fa08923bdb26be03e6a0f6a3546622dd657d09aa
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