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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: transformers
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+ tags:
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+ - code
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+ - granite
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+ - mlx
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+ base_model: ibm-granite/granite-20b-code-base
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+ datasets:
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+ - bigcode/commitpackft
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+ - TIGER-Lab/MathInstruct
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+ - meta-math/MetaMathQA
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+ - glaiveai/glaive-code-assistant-v3
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+ - glaive-function-calling-v2
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+ - bugdaryan/sql-create-context-instruction
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+ - garage-bAInd/Open-Platypus
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+ - nvidia/HelpSteer
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+ metrics:
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+ - code_eval
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+ pipeline_tag: text-generation
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+ inference: true
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+ model-index:
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+ - name: granite-20b-code-instruct
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+ results:
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+ - task:
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+ type: text-generation
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+ dataset:
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+ name: HumanEvalSynthesis(Python)
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+ type: bigcode/humanevalpack
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+ metrics:
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+ - type: pass@1
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+ value: 60.4
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+ name: pass@1
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+ - type: pass@1
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+ value: 53.7
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+ name: pass@1
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+ - type: pass@1
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+ value: 58.5
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+ name: pass@1
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+ - type: pass@1
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+ value: 42.1
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+ name: pass@1
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+ - type: pass@1
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+ value: 45.7
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+ name: pass@1
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+ - type: pass@1
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+ value: 42.7
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+ name: pass@1
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+ - type: pass@1
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+ value: 44.5
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+ name: pass@1
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+ - type: pass@1
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+ value: 42.7
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+ name: pass@1
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+ - type: pass@1
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+ value: 49.4
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+ name: pass@1
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+ - type: pass@1
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+ value: 32.3
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+ name: pass@1
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+ - type: pass@1
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+ value: 42.1
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+ name: pass@1
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+ - type: pass@1
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+ value: 18.3
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+ name: pass@1
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+ - type: pass@1
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+ value: 43.9
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+ name: pass@1
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+ - type: pass@1
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+ value: 43.9
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+ name: pass@1
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+ - type: pass@1
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+ value: 45.7
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+ name: pass@1
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+ - type: pass@1
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+ value: 41.5
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+ name: pass@1
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+ - type: pass@1
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+ value: 41.5
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+ name: pass@1
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+ - type: pass@1
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+ value: 29.9
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+ name: pass@1
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+ ---
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+
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+ # mlx-community/granite-20b-code-instruct-8bit
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+
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+ The Model [mlx-community/granite-20b-code-instruct-8bit](https://huggingface.co/mlx-community/granite-20b-code-instruct-8bit) was converted to MLX format from [ibm-granite/granite-20b-code-instruct](https://huggingface.co/ibm-granite/granite-20b-code-instruct) using mlx-lm version **0.13.0**.
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+
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+ ## Use with mlx
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+
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+ ```bash
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+ pip install mlx-lm
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+ ```
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+
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+ ```python
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+ from mlx_lm import load, generate
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+
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+ model, tokenizer = load("mlx-community/granite-20b-code-instruct-8bit")
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+ response = generate(model, tokenizer, prompt="hello", verbose=True)
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+ ```
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+ {
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+ "activation_function": "gelu_pytorch_tanh",
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+ "architectures": [
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+ "GPTBigCodeForCausalLM"
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+ ],
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+ "model_type": "gpt_bigcode",
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+ "multi_query": true,
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+ "scale_attn_weights": true,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.41.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 49152
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+ }
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