---
license: apache-2.0
datasets:
- simplescaling/s1K-1.1
- nvidia/OpenMathReasoning
- mlabonne/FineTome-100k
language:
- en
library_name: transformers
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- text-generation-inference
- math
- sft
- code
---

![zdfbdccf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4XCMQEsE0mv2s5rx-YdIK.png)

# Crux-Qwen3\_OpenThinking-4B

> **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.

> [!note]
> GGUF  : [https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF](https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF)

## Key Features

1. **Open and Structured Thinking**
   Fine-tuned on Gemini trajectory data and sk1.1 traces, enabling it to model complex thought processes, open reasoning, and multi-step problem-solving.

2. **Mathematical and Logical Reasoning**
   Trained with a focus on symbolic logic, arithmetic, and multi-step math problems, ideal for STEM education and technical domains.

3. **Code Understanding and Generation**
   Capable of writing, interpreting, and explaining code snippets in Python, JavaScript, and other languages with clarity.

4. **Factual Precision and Reliability**
   Curated datasets and reasoning benchmarks minimize hallucinations, ensuring trustworthy outputs for technical content.

5. **Instruction-Tuned for Clarity**
   Strong compliance with structured prompts, delivering step-by-step reasoning, formatted outputs (Markdown, JSON, tables), and clear explanations.

6. **Multilingual Capabilities**
   Supports over 20 languages for educational and technical translations across diverse linguistic contexts.

7. **Optimized Efficiency**
   Utilizes the 4B parameter Qwen3 base for resource-friendly deployment while maintaining strong reasoning performance.

## Quickstart with Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Crux-Qwen3_OpenThinking-4B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the thought process behind solving: If 5x - 3 = 2x + 12, find x."

messages = [
    {"role": "system", "content": "You are an open thinking tutor who explains reasoning clearly."},
    {"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

* Advanced open and logical reasoning
* Educational STEM tutoring and math problem solving
* Code assistance, explanation, and debugging
* Structured content generation (JSON, Markdown, tables)
* Multilingual reasoning and translation
* Lightweight, efficient deployment for reasoning tasks

## Limitations

* Less suited for highly creative or fictional content generation
* May require clear, unambiguous prompts for best results
* Smaller context window relative to larger models (14B+)
* Possible occasional factual inaccuracies in rare edge cases

## References

1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)