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Cherrytest
2025-08-25 18:31:20 +00:00
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.gitattributes vendored
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@@ -45,3 +45,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text
merges.txt filter=lfs diff=lfs merge=lfs -text
tokenizer.json filter=lfs diff=lfs merge=lfs -text
vocab.json filter=lfs diff=lfs merge=lfs -text

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README.md
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--- ---
license: Apache License 2.0 license: apache-2.0
base_model:
#model-type: - Qwen/Qwen3-1.7B
##如 gpt、phi、llama、chatglm、baichuan 等 datasets:
#- gpt - prithivMLmods/Demeter-LongCoT-400K
language:
#domain: - en
##如 nlp、cv、audio、multi-modal pipeline_tag: text-generation
#- nlp library_name: transformers
tags:
#language: - text-generation-inference
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa - LongCoT
#- cn - moe
- trl
#metrics: - math
##如 CIDEr、Blue、ROUGE 等 - code
#- CIDEr - stem
#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
--- ---
### 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 ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YL9ww0vwTra8q-9b8wGqd.png)
```bash
#安装ModelScope # **Demeter-LongCoT-Qwen3-1.7B**
pip install modelscope
``` > **Demeter-LongCoT-Qwen3-1.7B** is a reasoning-focused model fine-tuned on **Qwen/Qwen3-1.7B** using the **Demeter-LongCoT-400K** dataset.
> It is designed for **math and code chain-of-thought reasoning**, blending symbolic precision, scientific logic, and structured output fluency—making it an effective tool for developers, educators, and researchers seeking reliable step-by-step reasoning.
> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Demeter-LongCoT-Qwen3-1.7B-GGUF](https://huggingface.co/prithivMLmods/Demeter-LongCoT-Qwen3-1.7B-GGUF)
---
## **Key Features**
1. **Unified Reasoning in Math & Code**
Fine-tuned on **Demeter-LongCoT-400K**, which emphasizes extended chain-of-thought reasoning in mathematics, algorithms, and programming workflows.
2. **Advanced Code Understanding & Generation**
Handles multi-language programming tasks with explanations, optimization hints, and error detection—suited for algorithm synthesis, debugging, and prototyping.
3. **Mathematical Problem Solving**
Excels at step-by-step derivations, symbolic manipulations, and applied problem solving across calculus, algebra, and logic-based reasoning.
4. **Chain-of-Thought Focused Reasoning**
Optimized to produce clear, structured thought processes for both **STEM explanations** and **computational logic** tasks.
5. **Structured Output Mastery**
Generates well-formed outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, enabling smooth integration with research pipelines and technical documentation.
6. **Balanced Performance for Deployment**
Designed to deliver strong reasoning under moderate compute budgets, deployable on **mid-range GPUs**, **offline clusters**, and **specialized edge AI systems**.
---
## **Quickstart with Transformers**
```python ```python
#SDK模型下载 from transformers import AutoModelForCausalLM, AutoTokenizer
from modelscope import snapshot_download
model_dir = snapshot_download('prithivMLmods/Demeter-LongCoT-Qwen3-1.7B') model_name = "prithivMLmods/Demeter-LongCoT-Qwen3-1.7B"
```
Git下载 model = AutoModelForCausalLM.from_pretrained(
``` model_name,
#Git模型下载 torch_dtype="auto",
git clone https://www.modelscope.cn/prithivMLmods/Demeter-LongCoT-Qwen3-1.7B.git device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve the integral of x^2 * e^x step by step."
messages = [
{"role": "system", "content": "You are a tutor skilled in math, code, and step-by-step reasoning."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
``` ```
<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> ---
## **Intended Use**
* Step-by-step math tutoring and symbolic derivation
* Advanced coding assistant for algorithms, debugging, and structured reasoning
* Chain-of-thought generation for research and education tools
* Producing structured outputs for technical documentation and computational pipelines
* Deployments requiring reliable reasoning under constrained compute
## **Limitations**
* Not tuned for general-purpose or conversational tasks
* May underperform in long-form multi-document contexts
* Specialized in math and code—general writing or casual dialogue may be weak
* Prioritizes structured reasoning over natural or emotional tone generation

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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 %}
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{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
{%- set current_content = message.content if message.content is not none else '' %}
{%- set tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = current_content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (current_content|length) - tool_end_length %}
{%- if start_pos < 0 %}
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{%- endif %}
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{%- endif %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
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{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).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' }}
{{- message.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": [
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],
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"torch_dtype": "float16",
"transformers_version": "4.55.2",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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

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