README.md

metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
base_model:
  - OpenPipe/mistral-ft-optimized-1227
  - Intel/neural-chat-7b-v3-3
  - openchat/openchat-3.5-0106
  - openaccess-ai-collective/DPOpenHermes-7B-v2
  - mlabonne/NeuralHermes-2.5-Mistral-7B
  - cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
  - Open-Orca/Mistral-7B-OpenOrca

Darewin-7B-v2

Darewin-7B-v2 is a merge of the following models using LazyMergekit:

• OpenPipe/mistral-ft-optimized-1227
• Intel/neural-chat-7b-v3-3
• openchat/openchat-3.5-0106
• openaccess-ai-collective/DPOpenHermes-7B-v2
• mlabonne/NeuralHermes-2.5-Mistral-7B
• cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
• Open-Orca/Mistral-7B-OpenOrca

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-Instruct-v0.2
    # No parameters necessary for base model
  - model: OpenPipe/mistral-ft-optimized-1227
    parameters:
      density: 0.6
      weight: 0.25
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.55
      weight: 0.2
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.5
      weight: 0.2
  - model: openaccess-ai-collective/DPOpenHermes-7B-v2
    parameters:
      density: 0.45
      weight: 0.1
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.4
      weight: 0.1
  - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
    parameters:
      density: 0.4
      weight: 0.1
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      density: 0.3
      weight: 0.05
    
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Darewin-7B-v2"
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"])