Text Generation
MLX
fine-tune
finance
reconciliation
lora
structured-outputs
json-mode
agent-evaluation
severity-weighted
Eval Results (legacy)
Instructions to use caiotheodoro/reconforge-recon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use caiotheodoro/reconforge-recon-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("caiotheodoro/reconforge-recon-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 caiotheodoro/reconforge-recon-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 "caiotheodoro/reconforge-recon-lora" --prompt "Once upon a time"
- Atomic Chat