NeuralHermes-2.5-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
NeuralHermes-2.5-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
Eval
| Groups |Version|Filter|n-shot| Metric | Value | |Stderr|
|------------------|-------|------|-----:|-----------|------:|---|-----:|
|ai2_arc |N/A |none | 0|acc | 0.7508|± |0.0419|
| | |none | 0|acc_norm | 0.7393|± |0.0354|
|mmlu |N/A |none | 0|acc | 0.6082|± |0.1381|
| - humanities |N/A |none | 0|acc | 0.5545|± |0.1585|
| - other |N/A |none | 0|acc | 0.6823|± |0.1122|
| - social_sciences|N/A |none | 0|acc | 0.7062|± |0.0825|
| - stem |N/A |none | 0|acc | 0.5195|± |0.1231|
|truthfulqa |N/A |none | 0|acc | 0.5058|± |0.0023|
| | |none | 0|bleu_max |25.2659|± |0.7944|
| | |none | 0|bleu_acc | 0.5557|± |0.0174|
| | |none | 0|bleu_diff | 4.5134|± |0.7505|
| | |none | 0|rouge1_max |51.5877|± |0.8677|
| | |none | 0|rouge1_acc | 0.5496|± |0.0174|
| | |none | 0|rouge1_diff| 6.8850|± |1.0155|
| | |none | 0|rouge2_max |36.0848|± |1.0385|
| | |none | 0|rouge2_acc | 0.4700|± |0.0175|
| | |none | 0|rouge2_diff| 5.8893|± |1.1296|
| | |none | 0|rougeL_max |48.4591|± |0.8901|
| | |none | 0|rougeL_acc | 0.5496|± |0.0174|
| | |none | 0|rougeL_diff| 6.5791|± |1.0249|
🧩 Configuration
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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/NeuralHermes-2.5-Mistral-7B-Mistral-7B-Instruct-v0.2-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"])