Satyr-7B-Model_Stock
Satyr-7B-Model_Stock is a merge of the following models using LazyMergekit:
𧩠Configuration
models:
- model: NeverSleep/Noromaid-7B-0.4-DPO
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
- model: Undi95/Toppy-M-7B
- model: Epiculous/Fett-uccine-7B
merge_method: model_stock
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DreadPoor/Satyr-7B-Model_Stock"
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. | 71.74 |
AI2 Reasoning Challenge (25-Shot) | 68.60 |
HellaSwag (10-Shot) | 86.96 |
MMLU (5-Shot) | 65.02 |
TruthfulQA (0-shot) | 63.76 |
Winogrande (5-shot) | 80.43 |
GSM8k (5-shot) | 65.66 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.600
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.960
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.020
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard63.760
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.430
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard65.660