Instructions to use ApplauseLab/bankai-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ApplauseLab/bankai-v1 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("ApplauseLab/bankai-v1") 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 ApplauseLab/bankai-v1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "ApplauseLab/bankai-v1" --prompt "Once upon a time"
- Atomic Chat
| # QLoRA for Qwen3-Coder-Next on Apple Silicon with 128 GB unified memory. | |
| model: mlx-community/Qwen3-Coder-Next-4bit | |
| train: true | |
| data: artifacts/bankai-v1/data | |
| fine_tune_type: lora | |
| optimizer: adamw | |
| mask_prompt: true | |
| num_layers: 16 | |
| batch_size: 1 | |
| grad_accumulation_steps: 2 | |
| iters: 612 | |
| val_batches: 8 | |
| learning_rate: 1.0e-5 | |
| steps_per_report: 5 | |
| steps_per_eval: 100 | |
| save_every: 100 | |
| adapter_path: artifacts/bankai-v1/adapter | |
| max_seq_length: 2048 | |
| grad_checkpoint: true | |
| clear_cache_threshold: 68719476736 | |
| seed: 42 | |
| lora_parameters: | |
| rank: 8 | |
| dropout: 0.0 | |
| scale: 16.0 | |
| keys: | |
| - linear_attn.in_proj_qkvz | |
| - linear_attn.in_proj_ba | |
| - linear_attn.out_proj | |
| - self_attn.q_proj | |
| - self_attn.k_proj | |
| - self_attn.v_proj | |
| - self_attn.o_proj | |