Blur-7b-v1.21
Blur-7b-v1.21 is a merge of the following models using LazyMergekit:
𧩠Configuration
models:
- model: udkai/Turdus
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: liminerity/Blur-7b-v1.2
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: fblgit/UNA-TheBeagle-7b-v1
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/Blur-7b-v1.21"
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. | 74.18 |
AI2 Reasoning Challenge (25-Shot) | 70.82 |
HellaSwag (10-Shot) | 88.07 |
MMLU (5-Shot) | 64.85 |
TruthfulQA (0-shot) | 67.99 |
Winogrande (5-shot) | 83.82 |
GSM8k (5-shot) | 69.52 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.820
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.070
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.850
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard67.990
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.820
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.520