---
license: apache-2.0
datasets:
- xl-zhao/PromptCoT-QwQ-Dataset
- qingy2024/QwQ-LongCoT-Verified-130K
- PowerInfer/QWQ-LONGCOT-500K
- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-QwQ
library_name: transformers
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- cot
- text-generation-inference
- moe
- code
- math
---

![A.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_C9kmHWXdtsanwvuqtltO.png)

# Tureis-Qwen3\_QWQ-4B-Exp

> **Tureis-Qwen3\_QWQ-4B-Exp** is a fine-tuned variant of the **Qwen3-4B** architecture, trained specifically on **QWQ Synthetic datasets** to maximize **precise mathematical and logical reasoning**. This experimental model offers high accuracy on structured reasoning tasks while maintaining lightweight performance, making it ideal for technical, educational, and symbolic computation applications.

> [!note]
GGUF : https://huggingface.co/prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp-Q4_K_S-GGUF

## Key Features

1. **Precision Reasoning with QWQ Dataset**
   Tailored for high-fidelity symbolic reasoning, step-by-step math problem solving, and logic tasks, thanks to specialized QWQ synthetic fine-tuning.

2. **Lightweight Code Understanding**
   Capable of interpreting, generating, and correcting code in Python, C++, and other languages, optimized for concise logic-based tasks.

3. **Structured Output Formatting**
   Generates well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for notebooks, documentation, and data-centric workflows.

4. **Instruction-Following Accuracy**
   Tuned to follow multi-step user instructions with consistency across tasks and sessions, improving reliability in educational and factual domains.

5. **Multilingual Capabilities**
   Supports reasoning and generation in more than 20 languages for global accessibility and technical translation use cases.

6. **Efficient 4B Architecture**
   Based on Qwen3-4B, providing an optimal tradeoff between performance and compute requirements—suitable for mid-tier GPUs or scaled inference scenarios.

## Quickstart with Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp"

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

prompt = "If 5(x - 2) = 3x + 4, solve for x step-by-step."

messages = [
    {"role": "system", "content": "You are a precise reasoning assistant trained on QWQ datasets."},
    {"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

* Step-by-step math and logic problem solving
* Code snippet generation and explanation
* Technical and structured documentation
* JSON/Markdown/tabular output generation
* Education tools and auto-tutoring in STEM
* Multilingual reasoning and Q\&A systems

## Limitations

* Limited creativity for fiction or open-domain chat
* Small context window compared to larger models
* Sensitive to formatting in complex queries
* May still produce errors in adversarial reasoning prompts

## References

1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
2. [YaRN: Context Window Extension for LLMs](https://arxiv.org/pdf/2309.00071)