solidrust / Lorge-2x7B-UAMM-AWQ

huggingface.co
Total runs: 15
24-hour runs: 0
7-day runs: -6
30-day runs: -4
Model's Last Updated: September 03 2024
text-generation

Introduction of Lorge-2x7B-UAMM-AWQ

Model Details of Lorge-2x7B-UAMM-AWQ

Alsebay/Lorge-2x7B-UAMM AWQ

Model Summary

This is a merge of pre-trained language models created using mergekit .

This model was merged using the passthrough merge method.

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/Lorge-2x7B-UAMM-AWQ"
system_message = "You are Lorge-2x7B-UAMM, incarnated as a powerful AI. You were created by Alsebay."

# 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 Lorge-2x7B-UAMM-AWQ on huggingface.co

15
Total runs
0
24-hour runs
0
3-day runs
-6
7-day runs
-4
30-day runs

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