---
base_model: karakuri-ai/karakuri-lm-8x7b-instruct-v0.1
datasets:
- databricks/databricks-dolly-15k
- glaiveai/glaive-code-assistant-v3
- glaiveai/glaive-function-calling-v2
- gretelai/synthetic_text_to_sql
- meta-math/MetaMathQA
- microsoft/orca-math-word-problems-200k
- neural-bridge/rag-dataset-12000
- neural-bridge/rag-hallucination-dataset-1000
- nvidia/HelpSteer
- OpenAssistant/oasst2
language:
- en
- ja
library_name: transformers
license: apache-2.0
tags:
- mixtral
- steerlm
- mlx
---

# mlx-community/karakuri-lm-8x7b-instruct-v0.1-4bit

The Model [mlx-community/karakuri-lm-8x7b-instruct-v0.1-4bit](https://huggingface.co/mlx-community/karakuri-lm-8x7b-instruct-v0.1-4bit) was converted to MLX format from [karakuri-ai/karakuri-lm-8x7b-instruct-v0.1](https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1) using mlx-lm version **0.19.0**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/karakuri-lm-8x7b-instruct-v0.1-4bit")

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)
```