Instructions to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000

SGLang

How to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000 with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/Fireball-R1-LLama-3.1-8B-Freedom-9000
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

Features
Installation
Usage
Training Details
Ethical Considerations
License
Citation
Contact

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 episteme.ai@proton.me

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.

Downloads last month: 18

Safetensors

Model size

8B params

Tensor type

F16

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