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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
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
license: llama3.2
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base_model: meta-llama/Llama-3.2-1B-Instruct
---
### 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/Llama-3.2-1B-Instruct-MLXTuned
The Model [mlx-community/Llama-3.2-1B-Instruct-MLXTuned](https://huggingface.co/mlx-community/Llama-3.2-1B-Instruct-MLXTuned) was
converted to MLX format from [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
using mlx-lm version **0.20.5**.
## 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/Llama-3.2-1B-Instruct-MLXTuned')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/mlx-community/Llama-3.2-1B-Instruct-MLXTuned.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/Llama-3.2-1B-Instruct-MLXTuned")
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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"mlp_bias": false,
"model_type": "llama",
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"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
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},
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"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.45.0.dev0",
"use_cache": true,
"vocab_size": 128256
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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