初始化项目，由ModelHub XC社区提供模型
Model: WhirlwindAI/Qwen-R1-0.5B Source: Original Platform
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173
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- reasoning
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- chain-of-thought
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- qwen
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- tiny
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- whirlwindai
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pipeline_tag: text-generation
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datasets:
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- WhirlwindAI/Soft-CoT-1K
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library_name: transformers
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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---
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<div align="center">
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<img src="https://capsule-render.vercel.app/api?type=waving&height=220&color=gradient&customColorList=12,19,24,30&text=Qwen-R1-0.5B&fontSize=48&fontColor=ffffff&animation=twinkling"/>
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<img src="https://readme-typing-svg.demolab.com?font=Space+Grotesk&weight=700&size=27&duration=2300&pause=1200&color=A855F7¢er=true&vCenter=true&width=850&lines=Qwen-R1-0.5B;Reason+First.+Answer+Second.;Chain-of-Thought+on+a+Tiny+Model." />
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<img src="https://img.shields.io/badge/Parameters-0.5B-A855F7?style=for-the-badge">
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<img src="https://img.shields.io/badge/Base-Qwen2.5--0.5B-7C3AED?style=for-the-badge">
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<img src="https://img.shields.io/badge/Trained%20On-Soft--CoT--1K-06B6D4?style=for-the-badge">
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<img src="https://img.shields.io/badge/License-Apache--2.0-22C55E?style=for-the-badge">
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</div>
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---
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# 💡 The Idea
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<div align="center">
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> **Good answers come from good thinking.**
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Qwen-R1-0.5B is a fine-tuned version of Qwen2.5-0.5B-Instruct trained to **reason before it answers** using explicit `<thinking>` tags.
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</div>
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Instead of jumping straight to the answer, this model generates its reasoning first — making it more transparent, more reliable, and easier to debug.
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---
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# 🧠 How It Works
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Every response is structured as:
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```
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User: {question}
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Assistant: <thinking>{reasoning}</thinking>
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{answer}
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```
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The model learns to:
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1. **Think** – generate step-by-step reasoning
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2. **Answer** – provide the final response
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---
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# 📊 Training Details
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| Property | Value |
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|----------|-------|
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| Base Model | Qwen2.5-0.5B-Instruct |
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| Dataset | WhirlwindAI/Soft-CoT-1K |
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| Examples | 1,355 |
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| Method | QLoRA (4-bit) |
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| Epochs | 3 |
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| Learning Rate | 2e-4 |
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| LoRA Rank | 16 |
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| LoRA Alpha | 32 |
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---
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# 🚀 Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "WhirlwindAI/Qwen-R1-0.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
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prompt = "User: What is 2+2?\nAssistant: <thinking>"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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# 📋 Sample Output
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```
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User: What is the capital of France?
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Assistant: <thinking>Paris is the capital of France.</thinking>
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Paris
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```
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---
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# 📈 Performance
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The model was evaluated on 10 out-of-distribution questions:
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| Category | Performance |
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|----------|-------------|
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| Format (thinking tags) | ✅ Excellent |
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| General Knowledge | ✅ Good |
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| Creative Reasoning | ✅ Good |
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| Math/Logic | ⚠️ Needs improvement |
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| Physics/Science | ⚠️ Needs improvement |
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---
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# 🔬 What It Learned
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| Strength | Weakness |
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|----------|----------|
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| ✅ Consistent `<thinking>` format | ❌ Sometimes hallucinates facts |
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| ✅ Generates reasoning before answering | ❌ Struggles with multi-step math |
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| ✅ Retains general knowledge | ❌ Physics reasoning needs more data |
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---
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# 🧪 Test It Yourself
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```python
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questions = [
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"What is the capital of France?",
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"Explain entropy like I'm 5.",
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"Write a short poem about a robot.",
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]
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for q in questions:
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prompt = f"User: {q}\nAssistant: <thinking>"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=80)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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# 📜 Citation
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```bibtex
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@model{qwenr1_2026,
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title={Qwen-R1-0.5B},
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author={WhirlwindAI},
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year={2026},
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publisher={Hugging Face}
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}
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```
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---
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<div align="center">
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### 🌪️ WhirlwindAI
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**Efficient Models • Practical Research • Open AI**
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<br>
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<img src="https://capsule-render.vercel.app/api?type=waving&height=140§ion=footer&color=0:A855F7,100:06B6D4"/>
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</div>
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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|
"full_attention",
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|
"full_attention",
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"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
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"full_attention",
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"full_attention",
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|
"full_attention"
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|
],
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|
"max_position_embeddings": 32768,
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|
"max_window_layers": 21,
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|
"model_type": "qwen2",
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|
"num_attention_heads": 14,
|
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|
"num_hidden_layers": 24,
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|
"num_key_value_heads": 2,
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|
"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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|
"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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|
"sliding_window": null,
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|
"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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|
],
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|
"pad_token_id": 151643,
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|
"repetition_penalty": 1.1,
|
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|
"temperature": 0.7,
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|
"top_k": 20,
|
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|
"top_p": 0.8,
|
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|
"transformers_version": "5.12.1"
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|
}
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3
model.safetensors
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model.safetensors
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|
version https://git-lfs.github.com/spec/v1
|
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|
oid sha256:acdb65fcdf0d40c5dafc5f3d00a8c52af26268f079dcce763bddd36bd2ef5d16
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|
size 988097536
|
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3
tokenizer.json
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3
tokenizer.json
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|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
||||||
|
size 11421892
|
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30
tokenizer_config.json
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30
tokenizer_config.json
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|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
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|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"is_local": false,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|im_end|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user