Instructions to use ProSeAI/ProSe-Ohio-Legal-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ProSeAI/ProSe-Ohio-Legal-Expert 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("ProSeAI/ProSe-Ohio-Legal-Expert") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Unsloth Studio
How to use ProSeAI/ProSe-Ohio-Legal-Expert with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ProSeAI/ProSe-Ohio-Legal-Expert to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ProSeAI/ProSe-Ohio-Legal-Expert to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ProSeAI/ProSe-Ohio-Legal-Expert to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ProSeAI/ProSe-Ohio-Legal-Expert", max_seq_length=2048, ) - MLX LM
How to use ProSeAI/ProSe-Ohio-Legal-Expert with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "ProSeAI/ProSe-Ohio-Legal-Expert" --prompt "Once upon a time"
| { | |
| "adapter_path": "./adapters/ProSe-Ohio-Legal-Expert", | |
| "batch_size": 1, | |
| "clear_cache_threshold": 0, | |
| "config": null, | |
| "data": ".", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 1, | |
| "grad_checkpoint": true, | |
| "iters": 200, | |
| "learning_rate": 0.0001, | |
| "lora_parameters": { | |
| "rank": 8, | |
| "dropout": 0.0, | |
| "scale": 20.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": false, | |
| "max_seq_length": 2048, | |
| "model": "mlx-community/gemma-4-12B-it-4bit", | |
| "num_layers": 16, | |
| "optimizer": "adam", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": "./adapters/ProSe-Ohio-Legal-Expert/adapters.safetensors", | |
| "save_every": 100, | |
| "seed": 42, | |
| "steps_per_eval": 20, | |
| "steps_per_report": 10, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "trust_remote_code": false, | |
| "val_batches": 5 | |
| } |