metadata

base_model: timpal0l/BeagleCatMunin2
inference: false
library_name: transformers
merged_models:
  - bineric/NorskGPT-Mistral-7b
  - timpal0l/BeagleCatMunin
  - RJuro/munin-neuralbeagle-7b
pipeline_tag: text-generation
quantized_by: Suparious
tags:
  - 4-bit
  - AWQ
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
  - merge
  - mergekit
  - lazymergekit
  - bineric/NorskGPT-Mistral-7b
  - timpal0l/BeagleCatMunin
  - RJuro/munin-neuralbeagle-7b

timpal0l/BeagleCatMunin2 AWQ

Model creator: timpal0l
Original model: BeagleCatMunin2

Model Summary

BeagleCatMunin2 is a merge of the following models using LazyMergekit:

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/BeagleCatMunin2-AWQ"
system_message = "You are BeagleCatMunin2, incarnated as a powerful AI. You were created by timpal0l."

# 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