--- license: apache-2.0 language: - en library_name: transformers base_model: - Qwen/Qwen3-0.6B pipeline_tag: text-generation tags: - gspo - text-generation-inference - code - math - trl - science --- ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/nWBFPJp3zD0hao1cI9Axm.png) # **Cerium-Qwen3-R1-Dev** > Cerium-Qwen3-R1-Dev is a high-efficiency, multi-domain model fine-tuned on **Qwen-0.6B** using the **rStar-Coder** dataset, enhanced with **code expert clusters**, an extended **open code reasoning dataset**, and **DeepSeek R1 coding sample traces**. > This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute. > \[!note] > GGUF: [https://huggingface.co/prithivMLmods/Cerium-Qwen3-R1-Dev-GGUF](https://huggingface.co/prithivMLmods/Cerium-Qwen3-R1-Dev-GGUF) --- ## **Key Features** 1. **Unified Reasoning Across Code, Math & Science** Fine-tuned on **expert clusters** spanning programming, mathematics, and scientific logic, alongside **open code reasoning datasets** and **DeepSeek R1 coding sample traces**, boosting multi-modal symbolic reasoning. 2. **Advanced Code Reasoning & Generation** Supports multi-language coding with explanations, optimization hints, and error detection—ideal for full-stack prototyping, algorithm synthesis, and debugging workflows. 3. **Scientific Problem Solving** Performs analytical reasoning in physics, biology, and chemistry—explaining concepts, solving equations, and handling symbolic derivations step-by-step. 4. **Hybrid Symbolic-AI Thinking** Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM tasks and complex prompt decomposition. 5. **Structured Output Mastery** Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for research reports, technical documentation, and data formats. 6. **Optimized Lightweight Footprint for Versatile Deployment** Strikes a balance between performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and advanced **edge AI systems**. --- ## **Quickstart with Transformers** ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Cerium-Qwen3-R1-Dev" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Explain the difference between Newtonian mechanics and quantum mechanics with examples." messages = [ {"role": "system", "content": "You are a scientific tutor skilled in code, math, and reasoning."}, {"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** * Scientific tutoring, computational logic, and mathematical education * Advanced coding assistant for algorithm design, code reviews, and documentation * Structured technical data generation across formats and fields * STEM-focused chatbot or API for research and education tools * Mid-resource deployment requiring high symbolic fidelity ## **Limitations** * Not tuned for general-purpose or long-form creative writing * Context limitations may hinder multi-document or full codebase analysis * Specialized in technical and symbolic tasks—general chat may underperform * Prioritizes structured reasoning over emotional or casual tone generation