Gemma-2-Ataraxy-Gemmasutra-9B-slerp
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "recoilme/Gemma-2-Ataraxy-Gemmasutra-9B-slerp"
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
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 29.87 |
IFEval (0-Shot) | 76.49 |
BBH (3-Shot) | 42.25 |
MATH Lvl 5 (4-Shot) | 1.74 |
GPQA (0-shot) | 10.74 |
MuSR (0-shot) | 12.39 |
MMLU-PRO (5-shot) | 35.63 |
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Model tree for recoilme/Gemma-2-Ataraxy-Gemmasutra-9B-slerp
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard76.490
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard42.250
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.740
- acc_norm on GPQA (0-shot)Open LLM Leaderboard10.740
- acc_norm on MuSR (0-shot)Open LLM Leaderboard12.390
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard35.630