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---
frameworks: PyTorch
tasks:
- text-generation
license: Apache License 2.0
base_model:
- zxcAsD01/AL-0
datasets:
- zxcAsD01/MiMo-V2-Flash_distill
language:
- zh
---
***
# AL-0 (Distilled on Qwen3)
<div align="center">
![visitors](https://visitor-badge.laobi.icu/badge?page_id=your-repo/AL-0)
[![GitHub Code License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
</div>
## 📌 模型介绍 (Model Introduction)
**AL-0** 是一个由 **zxcAsD01** 创建的学习性质的小型语言模型。该模型是基于 **Qwen3-0.6B** 架构进行蒸馏（Distillation）并结合常规训练集进行微调（Fine-tuning）得到的。
尽管体积小巧，AL-0 旨在通过蒸馏（Distillation）技术，在保持高效推理的同时，尽可能继承大模型的逻辑推理与对话能力。
**AL-0** 的前置模型为同为 **zxcAsD01** 创建的 **AL-0-Base** ， **AL-0-Base** 是一个纯基座模型，经过极少的训练即可从接近随机输出收敛到输出连贯文本。 **AL-0-SFT** 即为其 **Base** 模型经少量额外预训练以及较多的指令微调得到的。
### 模型架构参数 (Architecture)
**AL-0** 的关键参数如下：
* **Base Model:** Qwen3
* **Hidden Size:** 768
* **Num Hidden Layers:** 12
* **Num Attention Heads:** 12
* **Vocabulary Size:** 151,936
* **Context Length:** 40,96 tokens
## 🛠️ 使用说明 (Usage)
### 1. 环境依赖
请确保安装了最新的 `transformers` 库：
```bash
pip install transformers>=4.57.1
```
### 2. 加载模型与推理
您可以使用以下 Python 代码加载并使用 AL-0 模型：
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = "your-username/AL-0"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "system", "content": "You are AL-0, a helpful assistant."},
{"role": "user", "content": "你好，请介绍一下你自己。"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.8
)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```
### 3. 推理参数建议
根据 Qwen3 的特性，建议如下参数：
* **非思考模式 (Non-Thinking Mode):** `temperature=0.7`, `top_p=0.8`, `top_k=20`。
* **思考模式 (Thinking Mode):** 如果模型支持，可使用 `temperature=0.6`, `top_p=0.95`, `top_k=20`。
## 🌐 模型来源 (Model Source)
AL-0 模型是基于 **Qwen3** 系列模型进行蒸馏和微调得到的。
**硬件**
Intel Core Ultra9-285H 32G
RTX5070laptop 8G
* **基础模型 (Base Model):** Qwen3-0.6B
* **来源团队:** Qwen Team (阿里云)
* **原始模型特点:** Qwen3 是阿里团队的开源大语言模型系列，支持思考模式与非思考模式，预训练数据覆盖 119 种语言，总计约 36 万亿 Token。
本模型的开发得益于 Qwen3 强大的基础能力以及“强到弱蒸馏”技术，使得小型模型也能具备出色的性能。
## 📄 许可证 (License)
本模型基于 **Apache 2.0 许可证** 开源发布。
Qwen3 系列模型本身也遵循 Apache 2.0 许可证。Apache 2.0 是一个商业友好的开源许可证，允许用户自由使用、修改和分发模型，包括用于商业目的和盈利，前提是保留版权声明和免责声明。
详细条款请参见 [LICENSE](LICENSE) 文件。
## 🙏 致谢 (Acknowledgements)
* 感谢 **Qwen Team** 开源了强大的 Qwen3 模型系列。
* 感谢开源社区提供的训练数据集与工具支持。
***

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"dtype": "float32",
"eos_token_id": 151645,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 2304,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 12,
"model_type": "qwen3",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"num_key_value_heads": 6,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{"framework":"Pytorch","task":"text-generation"}

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"_from_model_config": true,
"eos_token_id": [
151645
],
"pad_token_id": 151643,
"transformers_version": "4.57.1"
}

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"<|im_end|>",
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vocab.json (Stored with Git LFS) Normal file
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