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"
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"adapter_path": "artifacts/askai-v1/adapter",
"batch_size": 1,
"clear_cache_threshold": 0,
"config": "configs/askai-v1-lora.yaml",
"data": "artifacts/askai-v1/data",
"fine_tune_type": "lora",
"grad_accumulation_steps": 2,
"grad_checkpoint": true,
"iters": 612,
"learning_rate": 1e-05,
"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"
]
},
"lr_schedule": null,
"mask_prompt": true,
"max_seq_length": 2048,
"model": "mlx-community/Qwen3-Coder-Next-4bit",
"num_layers": 16,
"optimizer": "adamw",
"optimizer_config": {
"adam": {},
"adamw": {},
"muon": {},
"sgd": {},
"adafactor": {}
},
"project_name": null,
"report_to": null,
"resume_adapter_file": null,
"save_every": 100,
"seed": 42,
"steps_per_eval": 100,
"steps_per_report": 5,
"test": false,
"test_batches": 500,
"train": true,
"val_batches": 8
} |