Text Generation
MLX
Safetensors
English
reward-model
judge-model
grpo
lora
spct
apple-silicon
Eval Results (legacy)
Instructions to use rachittshah/j1-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use rachittshah/j1-micro 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("rachittshah/j1-micro") 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 rachittshah/j1-micro with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "rachittshah/j1-micro" --prompt "Once upon a time"
- Xet hash:
- 59582b22893e76ff95134237cc76e1b5d677dafa7f8731056dbfa95b8bac4f36
- Size of remote file:
- 11.4 MB
- SHA256:
- de053c72a4ae289224ac988558897303006ceea1db22f30b97d0d856969ea6b9
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