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.

GGUF: https://huggingface.co/prithivMLmods/Demeter-LongCoT-Qwen3-1.7B-GGUF

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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.

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Quickstart with Transformers

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)

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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