Instructions to use macmacmacmac/VibeThinker-3B-BugBounty-Triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use macmacmacmac/VibeThinker-3B-BugBounty-Triage 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("macmacmacmac/VibeThinker-3B-BugBounty-Triage") 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 macmacmacmac/VibeThinker-3B-BugBounty-Triage with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "macmacmacmac/VibeThinker-3B-BugBounty-Triage" --prompt "Once upon a time"
| { | |
| "adapter_path": "adapters", | |
| "batch_size": 8, | |
| "clear_cache_threshold": 0, | |
| "config": "lora_config.yaml", | |
| "data": "data/sft", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 1, | |
| "grad_checkpoint": true, | |
| "iters": 2000, | |
| "learning_rate": 0.0001, | |
| "lora_parameters": { | |
| "keys": [ | |
| "self_attn.q_proj", | |
| "self_attn.k_proj", | |
| "self_attn.v_proj", | |
| "self_attn.o_proj", | |
| "mlp.gate_proj", | |
| "mlp.up_proj", | |
| "mlp.down_proj" | |
| ], | |
| "rank": 16, | |
| "scale": 20.0, | |
| "dropout": 0.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": true, | |
| "max_seq_length": 2048, | |
| "model": "WeiboAI/VibeThinker-3B", | |
| "num_layers": 36, | |
| "optimizer": "adamw", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": null, | |
| "save_every": 200, | |
| "seed": 13, | |
| "steps_per_eval": 200, | |
| "steps_per_report": 10, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "val_batches": 25 | |
| } |