初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- Floppanacci/QWQ-LongCOT-AIMO
+- qingy2024/QwQ-LongCoT-Verified-130K
+- PowerInfer/QWQ-LONGCOT-500K
+- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-QwQ
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- math
+- moe
+---
+
+![DFG.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/G3sOT3hywqnBeW7EeaRNx.png)
+
+# Hatshepsut-Qwen3\_QWQ-LCoT-4B
+
+> **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.
+
+> [!note]
+GGUF : https://huggingface.co/prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B-Q4_K_M-GGUF
+
+## Key Features
+
+1. **LCoT Prompting Mastery**
+   Specifically tuned to handle Least-to-Complexity-of-Thought prompting, encouraging granular reasoning from simple to complex steps in problem solving.
+
+2. **QWQ-Based Precision Reasoning**
+   Built on the QWQ synthetic datasets, ensuring high-fidelity outputs in symbolic logic, algebraic manipulation, and mathematical word problems.
+
+3. **Code Understanding & Logic Generation**
+   Interprets and writes concise, logically sound code snippets in Python, C++, and JavaScript, with special focus on algorithmic steps and edge case handling.
+
+4. **Structured Output Control**
+   Outputs responses in JSON, Markdown, LaTeX, and table formats, ideal for educational material, notebooks, and structured reasoning chains.
+
+5. **Multilingual Reasoning**
+   Supports over 20 languages, enabling STEM-based problem solving and translation tasks across global languages.
+
+6. **Efficient 4B Parameter Footprint**
+   Lightweight yet powerful—suitable for researchers, educators, and developers running on mid-tier GPUs (e.g., A10, 3090, or L4).
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve using LCoT: If 3x - 7 = 2(x + 1), what is the value of x?"
+
+messages = [
+    {"role": "system", "content": "You are a step-by-step reasoning assistant trained on QWQ datasets with LCoT support."},
+    {"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
+
+* LCoT-style multi-step problem solving
+* Algebra, geometry, and logic question answering
+* Code generation with algorithmic transparency
+* Educational tools for math and programming
+* Structured technical output in Markdown/LaTeX
+* Multilingual STEM tutoring and reasoning
+
+## Limitations
+
+* May be sensitive to poorly formatted prompts
+* Less creative for open-domain or fictional tasks
+* Smaller context window (compared to 14B+ variants)
+* Early-stage reasoning errors may propagate if not prompted clearly
+
+## References
+
+1. [QWQ Synthetic Dataset]– Specialized reasoning corpus (experimental)
+2. [LIMO: Less is More for Reasoning](https://arxiv.org/pdf/2502.03387)
+3. [AIMO-2 Math Benchmark – OpenMathReasoning](https://arxiv.org/pdf/2504.16891)
+4. [YaRN: Context Extension for LLMs](https://arxiv.org/pdf/2309.00071)