MoE & Merge
Collection
17 items
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Updated
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mistral-7b-merged-slerp is a merge of the following models:
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
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
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mychen76/mistral-7b-merged-slerp"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "why the sky is blue."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.09 |
AI2 Reasoning Challenge (25-Shot) | 67.75 |
HellaSwag (10-Shot) | 86.17 |
MMLU (5-Shot) | 64.05 |
TruthfulQA (0-shot) | 59.85 |
Winogrande (5-shot) | 80.19 |
GSM8k (5-shot) | 68.54 |