---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
base_model: vectionlabs/Salience-27B-R5
library_name: transformers
tags:
- multimodal
- vision-language
- reasoning
- thinking
- efficient-reasoning
- code
- software-engineering
- swe
- agentic
- terminal
- tool-use
- long-context
- qwen3.8
- thinking-efficiency
- mlx
- mlx-my-repo
model-index:
- name: Salience-27B-R5
  results: []
---

# McG-221/Salience-27B-R5-mlx-8Bit

The Model [McG-221/Salience-27B-R5-mlx-8Bit](https://huggingface.co/McG-221/Salience-27B-R5-mlx-8Bit) was converted to MLX format from [vectionlabs/Salience-27B-R5](https://huggingface.co/vectionlabs/Salience-27B-R5) using mlx-lm version **0.31.2**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("McG-221/Salience-27B-R5-mlx-8Bit")

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)
```