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