---
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](https://huggingface.co/GGorman/WizardCoder-33B-V1.1-Q8-mlx) was converted to MLX format from [WizardLMTeam/WizardCoder-33B-V1.1](https://huggingface.co/WizardLMTeam/WizardCoder-33B-V1.1) using mlx-lm version **0.19.1**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
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)
```