metadata

base_model: ResplendentAI/DaturaCookie_7B
datasets:
  - ResplendentAI/Luna_NSFW_Text
  - unalignment/toxic-dpo-v0.2
  - ResplendentAI/Synthetic_Soul_1k
  - grimulkan/theory-of-mind
  - lemonilia/LimaRP
  - PygmalionAI/PIPPA
inference: false
language:
  - en
library_name: transformers
license: other
merged_models:
  - ResplendentAI/Datura_7B
  - ChaoticNeutrals/Cookie_7B
pipeline_tag: text-generation
prompt_template: |
  <|im_start|>system
  {system_message}<|im_end|>
  <|im_start|>user
  {prompt}<|im_end|>
  <|im_start|>assistant
quantized_by: Suparious
tags:
  - mistral
  - 4-bit
  - AWQ
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
  - chatml
  - not-for-all-audiences

ResplendentAI/DaturaCookie_7B AWQ

Model creator: ResplendentAI
Original model: DaturaCookie_7B

Model Summary

Proficient at roleplaying and lightehearted conversation, this model is prone to NSFW outputs.

Vision/multimodal capabilities:

If you want to use vision functionality:

You must use the latest versions of Koboldcpp. To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo.

You can load the mmproj by using the corresponding section in the interface:

How to use

Install the necessary packages

pip install --upgrade autoawq autoawq-kernels

Example Python code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/DaturaCookie_7B-AWQ"
system_message = "You are DaturaCookie, incarnated as a powerful AI."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

Text Generation Webui - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant