blurstral-7b
blurstral-7b is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 32]
- model: liminerity/Blur-7b-slerp-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/blurstral-7b"
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. | 69.08 |
AI2 Reasoning Challenge (25-Shot) | 66.30 |
HellaSwag (10-Shot) | 85.38 |
MMLU (5-Shot) | 65.18 |
TruthfulQA (0-shot) | 53.40 |
Winogrande (5-shot) | 81.37 |
GSM8k (5-shot) | 62.85 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard66.300
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.380
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.180
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard53.400
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.370
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard62.850