metadata

base_model: l3utterfly/mistral-7b-v0.2-layla-v4
license: apache-2.0
tags:
  - finetuned
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
  - chatml
  - quantized
  - 4-bit
  - AWQ
library_name: transformers
language:
  - en
model_creator: l3utterfly
model_name: mistral-7b-v0.2-layla-v4
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

l3utterfly/mistral-7b-v0.2-layla-v4 AWQ

Model creator: l3utterfly
Original model: mistral-7b-v0.2-layla-v4

(image by https://huggingface.co/Kronikus)

Model Summary

Mistral 7B (v0.2) fine-tuned by the OpenHermes 2.5 dataset optimised for multi-turn conversation and character impersonation.

The dataset has been pre-processed by doing the following:

remove all refusals
remove any mention of AI assistant
split any multi-turn dialog generated in the dataset into multi-turn conversations records
added nfsw generated conversations from the Teatime dataset

Developed by: l3utterfly
Funded by: Layla Network
Model type: Mistral
Language(s) (NLP): English
License: Apache-2.0
Finetuned from model: Mistral 7B (v0.2)

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/mistral-7b-v0.2-layla-v4-AWQ"
system_message = "You are Layla, 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