README.md

metadata

metrics:
  - code_eval
library_name: transformers
tags:
  - code
  - mlx
base_model: WizardLMTeam/WizardCoder-33B-V1.1
model-index:
  - name: WizardCoder
    results:
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: openai_humaneval
        metrics:
          - type: pass@1
            value: 0.799
            name: pass@1
            verified: false

GGorman/WizardCoder-33B-V1.1-Q8-mlx

The Model GGorman/WizardCoder-33B-V1.1-Q8-mlx was converted to MLX format from WizardLMTeam/WizardCoder-33B-V1.1 using mlx-lm version 0.19.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("GGorman/WizardCoder-33B-V1.1-Q8-mlx")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)