README.md

metadata

license: apache-2.0
tags:
  - mergekit
  - mistralai/Mistral-7B-Instruct-v0.2
  - mistralai/Mistral-7B-Instruct-v0.2
base_model:
  - mistralai/Mistral-7B-Instruct-v0.2
  - mistralai/Mistral-7B-Instruct-v0.2

Aurora-10.7b_Base

Aurora-10.7b_Base is a merge of the following models: to create a 10.7b base model that can be trained.

• mistralai/Mistral-7B-Instruct-v0.2
• mistralai/Mistral-7B-Instruct-v0.2

Merged Evals: (Has Not Been Finetuned)

Aurora-10.7b_Base

• Avg: 63.98
• ARC: 62.88
• HellaSwag: 83.99
• MMLU: 60.24
• T-QA: 67.84
• Winogrande: 76.4
• GSM8K: 32.52

(OG)Donated Evals:

Mistral-7b-v0.2

• Avg: 65.71
• ARC: 63.14
• HellaSwag: 84.88
• MMLU: 60.78
• T-QA: 68.26
• Winogrande: 77.19
• GSM8K: 40.03

🧩 Configuration

slices:
  - sources:
    - model: mistralai/Mistral-7B-Instruct-v0.2
      layer_range: [0, 24]
  - sources:
    - model: mistralai/Mistral-7B-Instruct-v0.2
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Steelskull/Aurora_base_test"
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"])