112 lines
3.9 KiB
Markdown
112 lines
3.9 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- simplescaling/s1K-1.1
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- nvidia/OpenMathReasoning
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- mlabonne/FineTome-100k
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language:
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- en
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library_name: transformers
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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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tags:
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- text-generation-inference
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- math
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- sft
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- code
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---
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# Crux-Qwen3\_OpenThinking-4B
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> **Crux-Qwen3\_OpenThinking-4B** is fine-tuned on the **Qwen3-4B** architecture, optimized for advanced **open thinking**, **mathematical reasoning**, and **logical problem solving**. This model is trained on the traces of **sk1.1**, which include 1,000 entries from the **Gemini thinking trajectory**, combined with fine-tuning on 100k tokens of **open math reasoning** data. This makes it highly effective for nuanced reasoning, educational tasks, and complex problem-solving requiring clear thought processes.
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> [!note]
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> GGUF : [https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF](https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF)
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## Key Features
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1. **Open and Structured Thinking**
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Fine-tuned on Gemini trajectory data and sk1.1 traces, enabling it to model complex thought processes, open reasoning, and multi-step problem-solving.
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2. **Mathematical and Logical Reasoning**
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Trained with a focus on symbolic logic, arithmetic, and multi-step math problems, ideal for STEM education and technical domains.
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3. **Code Understanding and Generation**
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Capable of writing, interpreting, and explaining code snippets in Python, JavaScript, and other languages with clarity.
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4. **Factual Precision and Reliability**
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Curated datasets and reasoning benchmarks minimize hallucinations, ensuring trustworthy outputs for technical content.
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5. **Instruction-Tuned for Clarity**
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Strong compliance with structured prompts, delivering step-by-step reasoning, formatted outputs (Markdown, JSON, tables), and clear explanations.
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6. **Multilingual Capabilities**
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Supports over 20 languages for educational and technical translations across diverse linguistic contexts.
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7. **Optimized Efficiency**
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Utilizes the 4B parameter Qwen3 base for resource-friendly deployment while maintaining strong reasoning performance.
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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/Crux-Qwen3_OpenThinking-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 = "Explain the thought process behind solving: If 5x - 3 = 2x + 12, find x."
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messages = [
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{"role": "system", "content": "You are an open thinking tutor who explains reasoning clearly."},
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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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* Advanced open and logical reasoning
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* Educational STEM tutoring and math problem solving
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* Code assistance, explanation, and debugging
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* Structured content generation (JSON, Markdown, tables)
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* Multilingual reasoning and translation
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* Lightweight, efficient deployment for reasoning tasks
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## Limitations
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* Less suited for highly creative or fictional content generation
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* May require clear, unambiguous prompts for best results
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* Smaller context window relative to larger models (14B+)
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* Possible occasional factual inaccuracies in rare edge cases
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## References
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1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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