metadata

base_model: ChaoticNeutrals/Eris_Remix_DPO_7B
library_name: transformers
tags:
  - quantized
  - 4-bit
  - AWQ
  - transformers
  - pytorch
  - mistral
  - text-generation
  - conversational
  - autotrain_compatible
  - endpoints_compatible
  - text-generation-inference
  - chatml
license: other
language:
  - en
model_creator: ChaoticNeutrals
model_name: Eris_Remix_7B
model_type: mistral
pipeline_tag: text-generation
inference: false
prompt_template: |
  <|im_start|>system
  {system_message}<|im_end|>
  <|im_start|>user
  {prompt}<|im_end|>
  <|im_start|>assistant
quantized_by: Suparious

ChaoticNeutrals/Eris-Remix-7B-DPO AWQ

Model creator: ChaoticNeutrals
Original model: Eris-Remix-7B-DPO

Model Summary

Jeitral: "Eris, the Greek goddess of chaos and discord."

Notes: Model should be excellent for both RP/Chat related tasks. Seems to be working in both Alpaca/Chatml.

Collaborative effort from both @Jeiku and @Nitral involving what we currently felt were our best individual projects.

We hope you enjoy! - The Chaotic Neutrals.

Remix with DPO: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW

Trained for 200 steps/ 1 epoch

Base model used: https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B

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/Eris-Remix-7B-DPO-AWQ"
system_message = "You are Dolphin, a helpful AI assistant."

# 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