--- language: - en license: llama3.1 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit model-index: - name: Fireball-R1-Llama-3.1-8B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 44.27 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 10.27 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 31.12 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 0.0 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 1.43 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.28 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=EpistemeAI/Fireball-R1-Llama-3.1-8B name: Open LLM Leaderboard --- **Upgrade** version: [EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT](https://huggingface.co/EpistemeAI/Fireball-R1-Llama-3.1-8B-Medical-COT) ## Model Information # Fireball-R1-LLama-3.1-8B ![License](https://img.shields.io/badge/License-Apache%202.0-blue) ![Version](https://img.shields.io/badge/Version-1.0.0-green) 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. --- ## Table of Contents - [Features](#features) - [Installation](#installation) - [Usage](#usage) - [Training Details](#training-details) - [Ethical Considerations](#ethical-considerations) - [License](#license) - [Citation](#citation) - [Contact](#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 ```bash pip install transformers torch pip install accelerate pip install -U transformers ``` ### Basic Inference ```bash from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B") model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1-Llama-3.1-8B") 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") # 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. " 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) ``` ### Recommended Parameters ```bash outputs = model.generate( **inputs, max_length=300, temperature=0.7, top_p=0.95, repetition_penalty=1.2 ) ``` # 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: - 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} } ``` For support or feedback: contact us at episteme.ai@proton.me This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Fireball-R1-Llama-3.1-8B-details) | Metric |Value| |-------------------|----:| |Avg. |14.73| |IFEval (0-Shot) |44.27| |BBH (3-Shot) |10.27| |MATH Lvl 5 (4-Shot)|31.12| |GPQA (0-shot) | 0.00| |MuSR (0-shot) | 1.43| |MMLU-PRO (5-shot) | 1.28|