Demeter-LongCoT-Qwen3-1.7B/README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen3-1.7B
datasets:
- prithivMLmods/Demeter-LongCoT-400K
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- LongCoT
- trl
- math
- code
- stem
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YL9ww0vwTra8q-9b8wGqd.png)

# **Demeter-LongCoT-Qwen3-1.7B**

> **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
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Demeter-LongCoT-Qwen3-1.7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    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)
```

---

## **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