Cetus-Qwen3_4B-GeneralThought

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.

[ GGUF ] : https://huggingface.co/prithivMLmods/Cetus-Qwen3_4B-GeneralThought-Q4_K_M-GGUF

Key Features

1. Broad Reasoning with GeneralThought-430K Trained on a carefully curated 430,000-sample dataset—GeneralThought-430K—spanning:

   • Mathematical and logical reasoning
   • Scientific and factual QA
   • Multistep instructions and problem decomposition
   • Abstract and applied reasoning tasks

2. Multi-Domain Task Versatility Equipped to handle use cases across STEM, humanities, code reasoning, and general knowledge workflows with consistency and structure.

3. Structured Output Control Outputs well-formatted answers in Markdown, LaTeX, JSON, and tabular formats, suitable for documentation, education, and technical reporting.

4. Instruction-Following with Multi-Step Fidelity Capable of following detailed prompts involving layered reasoning or procedural guidance with high step-to-step coherence.

5. Multilingual and Cross-Cultural Understanding Supports over 20 languages for global comprehension tasks and technical translation in education and public sector applications.

6. Efficient Qwen3-4B Base Offers an optimal balance between intelligence and computational efficiency—ideal for deployment on consumer-grade GPUs and scalable services.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Cetus-Qwen3_4B-GeneralThought"

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

prompt = "Explain the concept of entropy in thermodynamics in simple terms."

messages = [
    {"role": "system", "content": "You are a general-purpose reasoning assistant trained on GeneralThought-430K."},
    {"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

• General reasoning and educational Q&A
• Technical concept explanation and summarization
• Structured content generation in Markdown, LaTeX, and JSON
• Code and logic support in instruction-rich workflows
• Multi-language academic and public knowledge tools

Limitations

• Not optimized for purely creative or fictional content
• Smaller context window compared to frontier models
• May be sensitive to ambiguous or poorly structured prompts
• Can occasionally hallucinate in niche or adversarial scenarios

References

1. Qwen2.5 Technical Report – https://arxiv.org/pdf/2412.15115
2. YaRN: Context Window Extension – https://arxiv.org/pdf/2309.00071
3. GeneralThought-430K Dataset – (internal/prepublication dataset source, if applicable)