metadata
base_model:
- CultriX/NeuralTrix-7B-dpo
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
merge
This is a merge of pre-trained language models created using mergekit.
Code credit: this excellent medium blog
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using CultriX/NeuralTrix-7B-dpo as a base.
Models Merged
The following models were included in the merge:
- mlabonne/NeuralBeagle14-7B
- HuggingFaceH4/zephyr-7b-alpha
Benchmarks
Open LLM Leaderboard
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| mayacinka/NeuralZephyr-Beagle-7B | 71.57 | 68.6 | 86.38 | 64.67 | 65.17 | 81.14 | 63.46 |
Configuration
The following YAML configuration was used to produce this model:
models:
- model: CultriX/NeuralTrix-7B-dpo
- model: HuggingFaceH4/zephyr-7b-alpha
parameters:
density: 0.83
weight: 0.4
- model: mlabonne/NeuralBeagle14-7B
parameters:
density: 0.83
weight: 0.6
merge_method: dare_ties
base_model: CultriX/NeuralTrix-7B-dpo
parameters:
int8_mask: true
dtype: bfloat16
Inference
# pip install transformers
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/NeuralZephyr-Beagle-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"])