Instructions to use EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT 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-Medical-COT 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-Medical-COT")
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-Medical-COT")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT")
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

vLLM

How to use EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT 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-Medical-COT"
# 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-Medical-COT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT

SGLang

How to use EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT 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-Medical-COT" \
    --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-Medical-COT",
		"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-Medical-COT" \
        --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-Medical-COT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT 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-Medical-COT 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-Medical-COT 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-Medical-COT 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-Medical-COT",
    max_seq_length=2048,
)

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

Model Information

EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT

This is a state-of-the-art language model optimized for neutrality, STEM proficiency, and ethical alignment. 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. This is supervised fine tuned with medical chain of thought

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

Basic Inference


from transformers import AutoTokenizer, AutoModelForCausalLM  

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT")  
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT")  

prompt = "Calculate the molar mass of sulfuric acid (H₂SO₄)."  
inputs = tokenizer(prompt, return_tensors="pt")  
outputs = model.generate(**inputs, max_length=200)  
print(tokenizer.decode(outputs[0], skip_special_tokens=True))


##advance inference
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT")

# Load the model in 8-bit precision using bitsandbytes (requires a CUDA GPU)
model = AutoModelForCausalLM.from_pretrained(
    "EpistemeAI/Fireball-R1-Llama-3.1-8B",
    load_in_8bit=True,      # Enable 8-bit loading to reduce memory usage
    device_map="auto"       # Automatically map model layers to the available device(s)
)

# Define the system prompt and the user prompt
system_prompt = "You are a highly knowledgeable assistant with expertise in chemistry and physics. <think>"
user_prompt = "Calculate the molar mass of sulfuric acid (H₂SO₄)."

# Combine the system prompt with the user prompt. The format here follows a common convention for chat-like interactions.
full_prompt = f"System: {system_prompt}\nUser: {user_prompt}\nAssistant:"

# Tokenize the combined prompt and move the inputs to the GPU
inputs = tokenizer(full_prompt, return_tensors="pt").to("cuda")

# Generate output text from the model
outputs = model.generate(**inputs, max_length=12200)

# Decode and print the result, skipping special tokens
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Uploaded model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit

Ethical Considerations

Do Not Use For:

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.

Uploaded model

Developed by: EpistemeAI
License: apache-2.0
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: 6

Safetensors

Model size

8B params

Tensor type

F16

Model tree for EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT

Base model

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

Finetuned

(3)

this model

Quantizations

3 models

EpistemeAI
/

Fireball-R1-Llama-3.1-8B-Medical-COT

Model Information

EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT

Table of Contents

Features

Installation

Basic Inference

Uploaded model

Ethical Considerations

Limitations:

Thank you

License

Uploaded model

Model tree for EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT

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