Instructions to use Ailiance-fr/apertus-math-reasoning-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ailiance-fr/apertus-math-reasoning-lora with PEFT:
Task type is invalid.
- MLX
How to use Ailiance-fr/apertus-math-reasoning-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("Ailiance-fr/apertus-math-reasoning-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use Ailiance-fr/apertus-math-reasoning-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 "Ailiance-fr/apertus-math-reasoning-lora" --prompt "Once upon a time"
| { | |
| "fine_tune_type": "lora", | |
| "lora_parameters": { | |
| "rank": 16, | |
| "alpha": 32, | |
| "dropout": 0.05, | |
| "scale": 2.0 | |
| }, | |
| "num_layers": 16, | |
| "lora_layers": [ | |
| "self_attn.q_proj", | |
| "self_attn.k_proj", | |
| "self_attn.v_proj", | |
| "self_attn.o_proj" | |
| ] | |
| } |