solidrust / BeagleCatMunin2-AWQ

huggingface.co
Total runs: 14
24-hour runs: 0
7-day runs: 0
30-day runs: 12
Model's Last Updated: September 03 2024
text-generation

Introduction of BeagleCatMunin2-AWQ

Model Details of BeagleCatMunin2-AWQ

timpal0l/BeagleCatMunin2 AWQ

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:

Runs of solidrust BeagleCatMunin2-AWQ on huggingface.co

14
Total runs
0
24-hour runs
0
3-day runs
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7-day runs
12
30-day runs

More Information About BeagleCatMunin2-AWQ huggingface.co Model

BeagleCatMunin2-AWQ huggingface.co

BeagleCatMunin2-AWQ huggingface.co is an AI model on huggingface.co that provides BeagleCatMunin2-AWQ's model effect (), which can be used instantly with this solidrust BeagleCatMunin2-AWQ model. huggingface.co supports a free trial of the BeagleCatMunin2-AWQ model, and also provides paid use of the BeagleCatMunin2-AWQ. Support call BeagleCatMunin2-AWQ model through api, including Node.js, Python, http.

BeagleCatMunin2-AWQ huggingface.co Url

https://huggingface.co/solidrust/BeagleCatMunin2-AWQ

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solidrust BeagleCatMunin2-AWQ online free url in huggingface.co:

https://huggingface.co/solidrust/BeagleCatMunin2-AWQ

BeagleCatMunin2-AWQ install

BeagleCatMunin2-AWQ is an open source model from GitHub that offers a free installation service, and any user can find BeagleCatMunin2-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of BeagleCatMunin2-AWQ install, users can directly use BeagleCatMunin2-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

BeagleCatMunin2-AWQ install url in huggingface.co:

https://huggingface.co/solidrust/BeagleCatMunin2-AWQ

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