Feynman-Grpo-Exp-GGUF

Feynman-Grpo-Exp-GGUF is based on the Qwen 0.5B modality architecture, designed to enhance the reasoning capabilities of smaller models. It has been fine-tuned using the GRPO trainer on the OpenAI GSM8K dataset, enabling the model to excel in reinforcement learning, complex reasoning, and logical problem-solving. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for advanced reasoning tasks, instruction-following, and text generation.

Key Improvements

1. Enhanced Knowledge and Expertise: Improved mathematical reasoning, coding proficiency, and structured data processing, particularly for reinforcement learning tasks.
2. Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).
3. Greater Adaptability: Better role-playing capabilities and resilience to diverse system prompts.
4. Long-Context Support: Handles up to 64K tokens and generates 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: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.
• Mathematical & Scientific Problem-Solving: Enhanced capabilities for calculations, theorem proving, and scientific queries.
• Code Generation & Debugging: Generates and optimizes code across multiple programming languages.
• Structured Data Analysis: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.
• Multilingual Applications: High proficiency in over 29 languages, enabling global-scale applications.
• Extended Content Generation: Supports detailed document writing, research reports, and instructional guides.

Limitations

1. Computational Requirements: Despite being a 5B-parameter model, it still requires a capable GPU for efficient inference.
2. Language-Specific Variability: Performance may vary across supported languages, especially for low-resource languages.
3. Potential Error Accumulation: Long-text generation can sometimes introduce inconsistencies over extended outputs.
4. Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.
5. Prompt Sensitivity: Outputs can depend on the specificity and clarity of the input prompt.