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"
Add fully fused BankAI Q4_K_M GGUF
Browse filesStandalone Qwen3-Coder-Next Q4_K_M GGUF with the bankai-v1 LoRA merged into 64 target matrices in layers 32-47.
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- gguf/bankai-v1-Q4_K_M.gguf +3 -0
.gitattributes
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mlx/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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gguf/bankai-v1-f16.gguf filter=lfs diff=lfs merge=lfs -text
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mlx/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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gguf/bankai-v1-f16.gguf filter=lfs diff=lfs merge=lfs -text
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gguf/bankai-v1-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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gguf/bankai-v1-Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:75f8200b83756e6f32565b4579c9bb07aa6aa591fca380bc3f85af1138102296
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size 48528320544
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