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MonarchPipe-7B-slerp
MonarchPipe-7B-slerp is a merge of the following models using LazyMergekit:
π Eval
Nous
Eval results from the Nous benchmark suite (performed using LLM AutoEval).
Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
---|---|---|---|---|---|
MonarchPipe-7B-slerp π | 58.77 | 46.12 | 74.89 | 66.59 | 47.49 |
AlphaMonarch-7B π | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 |
Monarch-7B π | 62.68 | 45.48 | 77.07 | 78.04 | 50.14 |
OpenHermes-2.5-Mistral-7B π | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
NeuralHermes-2.5-Mistral-7B π | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
𧩠Configuration
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1227
layer_range: [0, 32]
- model: mlabonne/AlphaMonarch-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1227
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
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ichigoberry/MonarchPipe-7B-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"])
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