Instructions to use TechxGenus/Seed-Coder-8B-Base-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TechxGenus/Seed-Coder-8B-Base-DWQ with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("TechxGenus/Seed-Coder-8B-Base-DWQ") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use TechxGenus/Seed-Coder-8B-Base-DWQ with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "TechxGenus/Seed-Coder-8B-Base-DWQ" --prompt "Once upon a time"
- Xet hash:
- 329bdfaad80d9838b60fb9c52c7682566dc3737b72cb931d04a95d45023592f2
- Size of remote file:
- 4.64 GB
- SHA256:
- 69ee9b8dd5e137c9e25126ae75b568560b68cea7fb10f8f8dced70eb14c8564b
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