README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - DiscoResearch/DiscoLM_German_7b_v1
  - DRXD1000/Phoenix
  - LeoLM/leo-mistral-hessianai-7b-chat
  - openaccess-ai-collective/DPOpenHermes-7B-v2
  - fblgit/una-cybertron-7b-v2-bf16
  - mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
  - DiscoResearch/DiscoLM_German_7b_v1
  - DRXD1000/Phoenix
  - LeoLM/leo-mistral-hessianai-7b-chat
  - openaccess-ai-collective/DPOpenHermes-7B-v2
  - fblgit/una-cybertron-7b-v2-bf16
  - mlabonne/NeuralHermes-2.5-Mistral-7B

GermanDare-7B

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

• DiscoResearch/DiscoLM_German_7b_v1
• DRXD1000/Phoenix
• LeoLM/leo-mistral-hessianai-7b-chat
• openaccess-ai-collective/DPOpenHermes-7B-v2
• fblgit/una-cybertron-7b-v2-bf16
• mlabonne/NeuralHermes-2.5-Mistral-7B

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: DiscoResearch/DiscoLM_German_7b_v1
    parameters:
      density: 0.6
      weight: 0.2
  - model: DRXD1000/Phoenix
    parameters:
      density: 0.6
      weight: 0.2
  - model: LeoLM/leo-mistral-hessianai-7b-chat
    parameters:
      density: 0.6
      weight: 0.1
  - model: openaccess-ai-collective/DPOpenHermes-7B-v2
    parameters:
      density: 0.6
      weight: 0.2
  - model: fblgit/una-cybertron-7b-v2-bf16
    parameters:
      density: 0.6
      weight: 0.2
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.6
      weight: 0.1
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayflowergmbh/GermanDare-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"])