EpistemeAI/Fireball-R1.1-Llama-3.1-8B

Model Information

Fireball-R1-LLama-3.1-8B-Freedom 9000

This is a state-of-the-art language model optimized for neutrality, STEM proficiency, and uncensored alignment. Post trained, Fine-tuned Deepseek-R1-distill-llama-8b-unsloth-bnb-4bit for science, chemistry, and mathematics with reduced cultural/political bias. This large language model is open source. Further SFT (Supervised Fined tuning) with datasets to be unbias.

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Table of Contents

• Features
• Installation
• Usage
• Training Details
• Ethical Considerations
• License
• Citation
• Contact

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Features

• Neutral Worldview: Minimizes political/cultural bias via globally diverse training data and human feedback.
• STEM Specialization: Enhanced performance in:
  • Chemistry: Reaction mechanisms, periodic trends, spectroscopy.
  • Mathematics: Equation solving, proofs, calculus.
  • General Science: Hypothesis generation, research summarization.
• Ethical Guardrails: Filters sensitive content and flags uncertain outputs.

---

Installation

pip install transformers torch
pip install accelerate
pip install -U transformers
pip install -U bitsandbytes

Basic Inference

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

# Create a BitsAndBytesConfig for 8-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_8bit=True
)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Freedom-9000")

if torch.cuda.is_available():
    from transformers import BitsAndBytesConfig
    bnb_config = BitsAndBytesConfig(load_in_8bit=True)
    model = AutoModelForCausalLM.from_pretrained(
        "EpistemeAI/Fireball-R1-Llama-3.1-8B-Freedom-9000",
        quantization_config=bnb_config,
        device_map="auto"
    )
else:
    # Fallback for CPU-only systems
    model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Freedom-9000")


# Define your system prompt and user prompt
system_prompt = "You are an unbiased expert with advanced knowledge.<think>\n"
user_prompt = "What happened in Tiananmen Square in 1989?"
full_prompt = system_prompt + user_prompt

# Tokenize the full prompt and move it to the model's device
input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(model.device)

# Generate output from the model
output_ids = model.generate(
    input_ids,
    max_length=100,         # Adjust as needed
    do_sample=True,         # Use sampling for more varied output
    temperature=0.7,        # Adjust for creativity
)

# Decode the generated tokens back into a string
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)

Ethical Considerations

Do Not Use For:

• Medical/legal advice without expert oversight.
• Generating partisan or culturally insensitive content.

Limitations:

• May occasionally produce plausible but incorrect scientific explanations.
• Not fully immune to subtle biases.

Thank you

We appreciate the companies as following: Unsloth, Meta and Deepseek.

License

This model is licensed under [apache-2.0] - see LICENSE for details.

Citation

@misc{Fireball-R1-Llama-3.1-8B,  
  author = {EpistemeAI},  
  title = {Fireball-R1-8B: A Neutral, Science-Optimized Language Model},  
  year = {2025},  
  url = {https://huggingface.co/EpistemeAI/Fireball-R1-Llama-3.1-8B-Freedom-9000}  
}

For support or feedback: contact us at [email protected]

Uploaded model

• Developed by: EpistemeAI
• License: llama3.1
• Finetuned from model : EpistemeAI/Fireball-R1-Llama-3.1-8B

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.