112 lines
3.9 KiB
Markdown
112 lines
3.9 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- Floppanacci/QWQ-LongCOT-AIMO
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- qingy2024/QwQ-LongCoT-Verified-130K
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- PowerInfer/QWQ-LONGCOT-500K
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- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-QwQ
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B
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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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- code
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- math
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- moe
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---
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# Hatshepsut-Qwen3\_QWQ-LCoT-4B
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> **Hatshepsut-Qwen3\_QWQ-LCoT-4B** is a fine-tuned variant of the **Qwen3-4B** architecture, explicitly trained on **QWQ Synthetic datasets** with support for **Least-to-Complexity-of-Thought (LCoT)** prompting. This model is optimized for **precise mathematical reasoning**, **logic-driven multi-step solutions**, and **structured technical outputs**, while being compute-efficient and instruction-aligned.
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> [!note]
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GGUF : https://huggingface.co/prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B-Q4_K_M-GGUF
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## Key Features
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1. **LCoT Prompting Mastery**
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Specifically tuned to handle Least-to-Complexity-of-Thought prompting, encouraging granular reasoning from simple to complex steps in problem solving.
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2. **QWQ-Based Precision Reasoning**
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Built on the QWQ synthetic datasets, ensuring high-fidelity outputs in symbolic logic, algebraic manipulation, and mathematical word problems.
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3. **Code Understanding & Logic Generation**
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Interprets and writes concise, logically sound code snippets in Python, C++, and JavaScript, with special focus on algorithmic steps and edge case handling.
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4. **Structured Output Control**
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Outputs responses in JSON, Markdown, LaTeX, and table formats, ideal for educational material, notebooks, and structured reasoning chains.
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5. **Multilingual Reasoning**
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Supports over 20 languages, enabling STEM-based problem solving and translation tasks across global languages.
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6. **Efficient 4B Parameter Footprint**
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Lightweight yet powerful—suitable for researchers, educators, and developers running on mid-tier GPUs (e.g., A10, 3090, or L4).
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## Quickstart with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B"
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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 using LCoT: If 3x - 7 = 2(x + 1), what is the value of x?"
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messages = [
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{"role": "system", "content": "You are a step-by-step reasoning assistant trained on QWQ datasets with LCoT support."},
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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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## Intended Use
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* LCoT-style multi-step problem solving
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* Algebra, geometry, and logic question answering
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* Code generation with algorithmic transparency
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* Educational tools for math and programming
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* Structured technical output in Markdown/LaTeX
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* Multilingual STEM tutoring and reasoning
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## Limitations
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* May be sensitive to poorly formatted prompts
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* Less creative for open-domain or fictional tasks
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* Smaller context window (compared to 14B+ variants)
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* Early-stage reasoning errors may propagate if not prompted clearly
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## References
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1. [QWQ Synthetic Dataset]– Specialized reasoning corpus (experimental)
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2. [LIMO: Less is More for Reasoning](https://arxiv.org/pdf/2502.03387)
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3. [AIMO-2 Math Benchmark – OpenMathReasoning](https://arxiv.org/pdf/2504.16891)
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4. [YaRN: Context Extension for LLMs](https://arxiv.org/pdf/2309.00071)
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