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"
Rename BankAI training configuration
Browse files- training_config.yaml +2 -2
training_config.yaml
CHANGED
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@@ -1,7 +1,7 @@
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# QLoRA for Qwen3-Coder-Next on Apple Silicon with 128 GB unified memory.
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model: mlx-community/Qwen3-Coder-Next-4bit
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train: true
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data: artifacts/
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fine_tune_type: lora
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optimizer: adamw
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mask_prompt: true
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@@ -14,7 +14,7 @@ learning_rate: 1.0e-5
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steps_per_report: 5
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steps_per_eval: 100
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save_every: 100
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adapter_path: artifacts/
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max_seq_length: 2048
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grad_checkpoint: true
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clear_cache_threshold: 68719476736
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# QLoRA for Qwen3-Coder-Next on Apple Silicon with 128 GB unified memory.
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model: mlx-community/Qwen3-Coder-Next-4bit
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train: true
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+
data: artifacts/bankai-v1/data
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fine_tune_type: lora
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optimizer: adamw
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mask_prompt: true
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steps_per_report: 5
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steps_per_eval: 100
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save_every: 100
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+
adapter_path: artifacts/bankai-v1/adapter
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max_seq_length: 2048
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grad_checkpoint: true
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clear_cache_threshold: 68719476736
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