metadata
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1227
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1227
- mlabonne/NeuralHermes-2.5-Mistral-7B
NeuralPipe-7B-slerp-v0.2
NeuralPipe-7B-slerp-v0.2 is a merge of the following models:
Eval
| Groups |Version|Filter|n-shot| Metric | Value | |Stderr|
|------------------|-------|------|-----:|-----------|------:|---|-----:|
|ai2_arc |N/A |none | 0|acc | 0.7554|± |0.0406|
| | |none | 0|acc_norm | 0.7573|± |0.0332|
|mmlu |N/A |none | 0|acc | 0.6188|± |0.1472|
| - humanities |N/A |none | 0|acc | 0.5645|± |0.1686|
| - other |N/A |none | 0|acc | 0.6987|± |0.1098|
| - social_sciences|N/A |none | 0|acc | 0.7215|± |0.0887|
| - stem |N/A |none | 0|acc | 0.5208|± |0.1392|
|truthfulqa |N/A |none | 0|acc | 0.4746|± |0.0024|
| | |none | 0|bleu_max |26.7118|± |0.8092|
| | |none | 0|bleu_acc | 0.4957|± |0.0175|
| | |none | 0|bleu_diff | 3.1016|± |0.8065|
| | |none | 0|rouge1_max |53.1171|± |0.8499|
| | |none | 0|rouge1_acc | 0.5055|± |0.0175|
| | |none | 0|rouge1_diff| 4.0629|± |1.0345|
| | |none | 0|rouge2_max |39.1331|± |1.0068|
| | |none | 0|rouge2_acc | 0.4492|± |0.0174|
| | |none | 0|rouge2_diff| 3.7457|± |1.1652|
| | |none | 0|rougeL_max |49.8547|± |0.8818|
| | |none | 0|rougeL_acc | 0.5006|± |0.0175|
| | |none | 0|rougeL_diff| 3.6422|± |1.0540|
🧩 Configuration
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1227
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-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
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2"
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"])