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---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
- mlx
library_name: transformers
base_model: ben-at-jorah/emergency-llama32-1b-finetune-rmsys3
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- ben-at-jorah/emergency-training-data_2024-11-20
---
# ben-at-jorah/emergency-llama32-1b-finetune-rmsys3_mlx-8bit
The Model [ben-at-jorah/emergency-llama32-1b-finetune-rmsys3_mlx-8bit](https://huggingface.co/ben-at-jorah/emergency-llama32-1b-finetune-rmsys3_mlx-8bit) was converted to MLX format from [ben-at-jorah/emergency-llama32-1b-finetune-rmsys3](https://huggingface.co/ben-at-jorah/emergency-llama32-1b-finetune-rmsys3) using mlx-lm version **0.19.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("ben-at-jorah/emergency-llama32-1b-finetune-rmsys3_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)
```
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