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---
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)
```
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