OmnicromsBrain/EverythingBagel-DPO-7B
- jondurbin/bagel-dpo-7b-v0.5
- SanjiWatsuki/Silicon-Maid-7B
  - chargoddard/loyal-piano-m7
  - NeverSleep/Noromaid-7b-v0.2
  - athirdpath/NSFW_DPO_vmgb-7b
  - xDAN-AI/xDAN-L1-Chat-RL-v1
OmnicromsBrain/ToppyCox-7B
- N8Programs/Coxcomb
- Undi95/Toppy-M-7B
  - openchat/openchat_3.5
  - NousResearch/Nous-Capybara-7B-V1.9
  - HuggingFaceH4/zephyr-7b-beta
  - Undi95/zephyr-7b-beta-pippa-sharegpt
  - Undi95/Nous-Capybara-7B-V1.9-120-Days
  - Undi95/openchat_3.5-LimaRP-13B
  - lemonilia/AshhLimaRP-Mistral-7B
  - mistralai/Mistral-7B-v0.1

BTW the name was suggested by Mistral 8x7b instruct

⚡ Quantized Models

.GGUF https://huggingface.co/OmnicromsBrain/Eros_Scribe-7b-GGUF

🧩 Configuration

yaml

slices:
  - sources:
      - model: OmnicromsBrain/EverythingBagel-DPO-7B
        layer_range: [0, 32]
      - model: OmnicromsBrain/ToppyCox-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: OmnicromsBrain/EverythingBagel-DPO-7B
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

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "OmnicromsBrain/Eros_Scribe-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"])