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Fugazi14b
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Quazim0t0/Fugazi14b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
#94 on leaderboards (WHEN THIS RESULT WAS POSTED , RANKINGS WILL ALWAYS CHANGE-2/27/2025) Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.94 |
| IFEval (0-Shot) | 69.98 |
| BBH (3-Shot) | 56.09 |
| MATH Lvl 5 (4-Shot) | 46.53 |
| GPQA (0-shot) | 13.53 |
| MuSR (0-shot) | 16.42 |
| MMLU-PRO (5-shot) | 49.08 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard69.980
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard56.090
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard46.530
- acc_norm on GPQA (0-shot)Open LLM Leaderboard13.530
- acc_norm on MuSR (0-shot)Open LLM Leaderboard16.420
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard49.080