Feynman-Grpo-Exp

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.

Key Improvements

1. Enhanced Knowledge and Expertise: Strengthened mathematical reasoning, code generation, and problem-solving skills, particularly in scientific and technical domains.
2. Fine-Tuned Instruction Following: Optimized for generating structured outputs like JSON and handling long-form text (up to 8K+ tokens).
3. Greater Adaptability: Enhanced role-playing capabilities, allowing for better responses to diverse prompts.
4. Long-Context Support: Capable of processing up to 64K tokens and generating up to 4K tokens per output.
5. Multilingual Proficiency: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, and more.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Feynman-Grpo-Exp"

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

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
    {"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

• Advanced Reasoning & Context Understanding: Ideal for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.
• Mathematical & Scientific Problem-Solving: Optimized for handling advanced calculations, theorem proving, and scientific queries.
• Code Generation & Debugging: Capable of generating and optimizing code across multiple programming languages.
• Structured Data Analysis: Processes structured data, including tables, JSON, and other formats, making it well-suited for data-centric tasks.
• Multilingual Applications: Proficient in over 29 languages, enabling a global scale for applications.
• Extended Content Generation: Supports detailed document writing, research reports, and instructional guides.

Limitations

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.
2. Language-Specific Variability: Performance may vary across supported languages, with possible challenges for low-resource languages.
3. Potential Error Accumulation: Long-text generation can introduce inconsistencies or errors over extended outputs.
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.
5. Prompt Sensitivity: Outputs depend heavily on the specificity and clarity of the input prompts.