Instructions to use batuhne/customer-support-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use batuhne/customer-support-llm 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("batuhne/customer-support-llm") 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 batuhne/customer-support-llm with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "batuhne/customer-support-llm" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- edcf59c3d173c887f231d8a6f72188cbea02f835be0926c9315e075e3885bf1a
- Size of remote file:
- 55.6 MB
- SHA256:
- a0aa2b113a92473e913788183cf3c6d3033b26cbe09f675a6f37a802f54b8c04
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