Instructions to use crimson-knight/SmolLM-135M-Instruct-4bit-amber-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use crimson-knight/SmolLM-135M-Instruct-4bit-amber-lora 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("crimson-knight/SmolLM-135M-Instruct-4bit-amber-lora") 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 crimson-knight/SmolLM-135M-Instruct-4bit-amber-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "crimson-knight/SmolLM-135M-Instruct-4bit-amber-lora" --prompt "Once upon a time"
Amber knowledge-pack LoRA adapter (mlx_lm, lr 1e-4, 2000 it) + verbatim before/after per-item eval reports
017771e verified - Xet hash:
- a88fb75c26d5fc8ccd4408c6b49288b7c2ff74a076ac615aec9f637c997d40f2
- Size of remote file:
- 2.62 MB
- SHA256:
- 8f88465b98309f4001d85b53eae959e4f22dae3155fc1e43a60f5b90deba6c76
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