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--- |
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license: apache-2.0 |
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datasets: |
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- openai/gsm8k |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-0.5B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- math |
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- reasoning |
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- grpo |
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- trl |
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- code |
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--- |
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# **Feynman-Grpo-Exp** |
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Feynman-Grpo-Exp is based on the Qwen 0.5B modality architecture, designed to enhance the reasoning capabilities of 0.5B-parameter models. It has been fine-tuned using the GRPO trainer on the OpenAI GSM8K dataset for reinforcement learning, improving its ability to handle complex reasoning tasks, multi-step problem-solving, and mathematical challenges. This model excels in chain-of-thought (CoT) reasoning and logical problem-solving, making it suitable for a variety of advanced tasks that require precise and structured outputs. |
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### **Key Improvements** |
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1. **Enhanced Knowledge and Expertise**: Strengthened mathematical reasoning, code generation, and problem-solving skills, particularly in scientific and technical domains. |
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2. **Fine-Tuned Instruction Following**: Optimized for generating structured outputs like JSON and handling long-form text (up to 8K+ tokens). |
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3. **Greater Adaptability**: Enhanced role-playing capabilities, allowing for better responses to diverse prompts. |
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4. **Long-Context Support**: Capable of processing up to **64K tokens** and generating up to **4K tokens** per output. |
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5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more. |
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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/Feynman-Grpo-Exp" |
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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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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Give me a short introduction to large language models." |
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messages = [ |
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{"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."}, |
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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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- **Advanced Reasoning & Context Understanding**: Ideal for logical deduction, multi-step problem-solving, and complex knowledge-based tasks. |
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- **Mathematical & Scientific Problem-Solving**: Optimized for handling advanced calculations, theorem proving, and scientific queries. |
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- **Code Generation & Debugging**: Capable of generating and optimizing code across multiple programming languages. |
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- **Structured Data Analysis**: Processes structured data, including tables, JSON, and other formats, making it well-suited for data-centric tasks. |
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- **Multilingual Applications**: Proficient in over 29 languages, enabling a global scale for applications. |
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- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides. |
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### **Limitations** |
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1. **Computational Requirements**: Despite being a **0.5B-parameter** model, it requires significant computational resources for efficient inference, especially when dealing with long-context processing. |
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2. **Language-Specific Variability**: Performance may vary across supported languages, with possible challenges for low-resource languages. |
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3. **Potential Error Accumulation**: Long-text generation can introduce inconsistencies or errors over extended outputs. |
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4. **Limited Real-World Awareness**: The model's knowledge is restricted to the training data, which may not reflect the most recent events or developments. |
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5. **Prompt Sensitivity**: Outputs depend heavily on the specificity and clarity of the input prompts. |
