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---
license: Apache License 2.0
#model-type:
##如 gpt、phi、llama、chatglm、baichuan 等
#- gpt
#domain:
##如 nlp、cv、audio、multi-modal
#- nlp
#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn
#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr
#tags:
##各种自定义,包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained
#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
language:
- en
license: apache-2.0
tags:
- mlx
base_model: alpindale/Mistral-7B-v0.2-hf
datasets:
- cognitivecomputations/dolphin
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- jondurbin/airoboros-2.2.1
- teknium/openhermes-2.5
- m-a-p/Code-Feedback
- m-a-p/CodeFeedback-Filtered-Instruction
model-index:
- name: dolphin-2.8-mistral-7b-v02
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.469
name: pass@1
verified: false
---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令或者ModelScope SDK来下载模型
SDK下载
# mlx-community/dolphin-2.8-mistral-7b-v02
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.8-mistral-7b-v02`]() using mlx-lm version **0.7.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) for more details on the model.
## Use with mlx
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('mlx-community/dolphin-2.8-mistral-7b-v02')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/mlx-community/dolphin-2.8-mistral-7b-v02.git
pip install mlx-lm
```
<p style="color: lightgrey;">如果您是本模型的贡献者,我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>,及时完善模型卡片内容。</p>
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.8-mistral-7b-v02")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```