---
title: Pearl-7B
canonical_url: "https://www.modelscope.cn/models/mlx-community/Pearl-7B"
md_url: "https://www.modelscope.cn/models/mlx-community/Pearl-7B.md"
repository: mlx-community/Pearl-7B
last_updated: 2024-12-19
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - mistral
architectures:
  - MistralForCausalLM
parameters: 1.2B
tensor_type:
  - U32
  - F16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 994
stars: 0
tags:
  - mlx
  - merge
  - mergekit
  - louisbrulenaudet/Pearl-7B-slerp
  - WizardLM/WizardMath-7B-V1.1
  - cognitivecomputations/WestLake-7B-v2-laser
  - CultriX/NeuralTrix-7B-dpo
  - chemistry
  - biology
  - math
---

# Pearl-7B

> Pearl-7B - mlx-community 在 ModelScope 开源的模型。mlx-community/Pearl-7B

mlx-community/Pearl-7B 是 ModelScope 魔搭社区上的 1.2B 参数text-generation模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/Pearl-7B
- **Tasks**: text-generation
- **Parameters**: 1.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: mlx, merge, mergekit, louisbrulenaudet/Pearl-7B-slerp, WizardLM/WizardMath-7B-V1.1, cognitivecomputations/WestLake-7B-v2-laser, CultriX/NeuralTrix-7B-dpo, chemistry, biology, math
- **Downloads**: 994
- **Stars**: 0
- **Last updated**: 2024-12-19

Source: https://www.modelscope.cn/models/mlx-community/Pearl-7B

---

<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>

# mlx-community/Pearl-7B

This model was converted to MLX format from [`louisbrulenaudet/Pearl-7B-0211-ties`]() using mlx-vlm version **0.15.2**.
Refer to the [original model card](louisbrulenaudet/Pearl-7B-0211-ties) for more details on the model.

## Use with mlx

```bash
pip install -U mlx-vlm
python -m mlx_vlm.generate --model  mlx-community/Pearl-7B --max-tokens 100 --temp 0.0
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Pearl-7B")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```

## Citing & Authors

If you use this code in your research, please use the following BibTeX entry.

```BibTeX
@misc{louisbrulenaudet2024,
  author =       {Louis Brulé Naudet},
  title =        {Pearl-7B-0211-ties, an xtraordinary 7B model},
  year =         {2024}
  howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-0211-ties}},
}
```

## Feedback

If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).