Eros_Scribe-7b-imatrix-GGUF

Quantizations of https://huggingface.co/OmnicromsBrain/Eros_Scribe-7b

Open source inference clients/UIs

• llama.cpp
• KoboldCPP
• ollama
• text-generation-webui
• jan
• GPT4All

Closed source inference clients/UIs

• LM Studio
• Backyard AI
• More will be added...

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From original readme

This model was created for the purpose of writing NSFW Prose but it's also very good at RP.

Over a dozen models and at least 25 dataset were involved in this merge. Eros_Scribe-7b is a merge of the following models:

• 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

🧩 Configuration

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

!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"])