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--- |
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license: apache-2.0 |
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datasets: |
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- GeneralReasoning/GeneralThought-430K |
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base_model: |
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- prithivMLmods/Qwen3-4B-ft-bf16 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- moe |
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- text-generation-inference |
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- code |
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- math |
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--- |
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# Cetus-Qwen3\_4B-GeneralThought |
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> Cetus-Qwen3\_4B-GeneralThought is a fine-tuned variant of the Qwen3-4B architecture, trained on the GeneralThought-430K dataset to enhance broad-spectrum reasoning, logical coherence, and structured multi-domain problem solving. This model is optimized for general-purpose tasks including instruction following, technical question answering, and reasoning-based generation across diverse knowledge fields. |
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> [!note] |
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[ GGUF ] : https://huggingface.co/prithivMLmods/Cetus-Qwen3_4B-GeneralThought-Q4_K_M-GGUF |
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## Key Features |
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1. Broad Reasoning with GeneralThought-430K |
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Trained on a carefully curated 430,000-sample dataset—GeneralThought-430K—spanning: |
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* Mathematical and logical reasoning |
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* Scientific and factual QA |
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* Multistep instructions and problem decomposition |
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* Abstract and applied reasoning tasks |
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2. Multi-Domain Task Versatility |
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Equipped to handle use cases across STEM, humanities, code reasoning, and general knowledge workflows with consistency and structure. |
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3. Structured Output Control |
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Outputs well-formatted answers in Markdown, LaTeX, JSON, and tabular formats, suitable for documentation, education, and technical reporting. |
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4. Instruction-Following with Multi-Step Fidelity |
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Capable of following detailed prompts involving layered reasoning or procedural guidance with high step-to-step coherence. |
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5. Multilingual and Cross-Cultural Understanding |
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Supports over 20 languages for global comprehension tasks and technical translation in education and public sector applications. |
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6. Efficient Qwen3-4B Base |
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Offers an optimal balance between intelligence and computational efficiency—ideal for deployment on consumer-grade GPUs and scalable services. |
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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/Cetus-Qwen3_4B-GeneralThought" |
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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 concept of entropy in thermodynamics in simple terms." |
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messages = [ |
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{"role": "system", "content": "You are a general-purpose reasoning assistant trained on GeneralThought-430K."}, |
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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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* General reasoning and educational Q\&A |
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* Technical concept explanation and summarization |
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* Structured content generation in Markdown, LaTeX, and JSON |
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* Code and logic support in instruction-rich workflows |
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* Multi-language academic and public knowledge tools |
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## Limitations |
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* Not optimized for purely creative or fictional content |
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* Smaller context window compared to frontier models |
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* May be sensitive to ambiguous or poorly structured prompts |
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* Can occasionally hallucinate in niche or adversarial scenarios |
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## References |
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1. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115) |
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2. YaRN: Context Window Extension – [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) |
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3. GeneralThought-430K Dataset – (internal/prepublication dataset source, if applicable) |
