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--- |
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license: apache-2.0 |
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datasets: |
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- smirki/UI_REASONING_v1.01 |
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- SynthLabsAI/Big-Math-RL-Verified |
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- open-r1/OpenR1-Math-220k |
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- HuggingFaceH4/MATH-500 |
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language: |
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- en |
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base_model: |
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- prithivMLmods/Viper-Coder-v1.1 |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- Non-Reasoning |
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- Raptor |
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- Coder |
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- X2 |
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- Html |
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- Css |
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- React |
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- Python |
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- Java |
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- Qwen |
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- Math |
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--- |
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# **Raptor-X2** |
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> [!warning] |
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> Non-Reasoning |
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> Raptor-X2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced mathematical explanations, scientific reasoning, and general-purpose coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor-X2 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. |
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Key improvements include: |
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1. **Enhanced Mathematical Reasoning**: Provides step-by-step explanations for complex mathematical problems, making it useful for students, researchers, and professionals. |
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2. **Advanced Scientific Understanding**: Excels in explaining scientific concepts across physics, chemistry, biology, and engineering. |
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3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation. |
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4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. |
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5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. |
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# **Quickstart with transformers** |
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Raptor-X2" |
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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 fundamental theorem of calculus." |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, |
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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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``` |
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# **Intended Use** |
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1. **Mathematical Explanation**: |
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Designed for providing step-by-step solutions to mathematical problems, including algebra, calculus, and discrete mathematics. |
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2. **Scientific Reasoning**: |
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Suitable for explaining scientific theories, conducting physics simulations, and solving chemistry equations. |
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3. **Programming and Software Development**: |
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Capable of generating, analyzing, and optimizing code in multiple programming languages. |
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4. **Educational Assistance**: |
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Helps students and researchers by providing explanations, summaries, and structured learning material. |
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5. **Multilingual Applications**: |
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Supports global communication, translations, and multilingual content generation. |
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6. **Long-Form Content Generation**: |
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Can generate extended responses, including research papers, documentation, and technical reports. |
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# **Limitations** |
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1. **Hardware Requirements**: |
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Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. |
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2. **Potential Bias in Responses**: |
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While designed to be neutral, outputs may still reflect biases present in training data. |
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3. **Complexity in Some Scientific Domains**: |
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While proficient in general science, highly specialized fields may require verification. |
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4. **Limited Real-World Awareness**: |
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Does not have access to real-time events beyond its training cutoff. |
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5. **Error Propagation in Extended Outputs**: |
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Minor errors in early responses may affect overall coherence in long-form outputs. |
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6. **Prompt Sensitivity**: |
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The effectiveness of responses may depend on how well the input prompt is structured. |
