solidrust / Aeryth-7B-v0.1-AWQ

huggingface.co
Total runs: 23
24-hour runs: 0
7-day runs: 0
30-day runs: 11
Model's Last Updated: September 03 2024
text-generation

Introduction of Aeryth-7B-v0.1-AWQ

Model Details of Aeryth-7B-v0.1-AWQ

Aeryth 7B v0.1 AWQ

Neural-Story

Model Details

Designed to generate instructive and narrative text, with a specific focus on roleplay & story writing.

Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2, with apache-2.0 license.

You may download and use this model for research and training purposes.

This model is NOT suitable for commercial deployment without additional safety.

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/Aeryth-7B-v0.1-AWQ"
system_message = "You are Aeryth, 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:

Prompt template: ChatML
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Runs of solidrust Aeryth-7B-v0.1-AWQ on huggingface.co

23
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
11
30-day runs

More Information About Aeryth-7B-v0.1-AWQ huggingface.co Model

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https://choosealicense.com/licenses/apache-2.0

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Aeryth-7B-v0.1-AWQ huggingface.co is an AI model on huggingface.co that provides Aeryth-7B-v0.1-AWQ's model effect (), which can be used instantly with this solidrust Aeryth-7B-v0.1-AWQ model. huggingface.co supports a free trial of the Aeryth-7B-v0.1-AWQ model, and also provides paid use of the Aeryth-7B-v0.1-AWQ. Support call Aeryth-7B-v0.1-AWQ model through api, including Node.js, Python, http.

Aeryth-7B-v0.1-AWQ huggingface.co Url

https://huggingface.co/solidrust/Aeryth-7B-v0.1-AWQ

solidrust Aeryth-7B-v0.1-AWQ online free

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solidrust Aeryth-7B-v0.1-AWQ online free url in huggingface.co:

https://huggingface.co/solidrust/Aeryth-7B-v0.1-AWQ

Aeryth-7B-v0.1-AWQ install

Aeryth-7B-v0.1-AWQ is an open source model from GitHub that offers a free installation service, and any user can find Aeryth-7B-v0.1-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of Aeryth-7B-v0.1-AWQ install, users can directly use Aeryth-7B-v0.1-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Aeryth-7B-v0.1-AWQ install url in huggingface.co:

https://huggingface.co/solidrust/Aeryth-7B-v0.1-AWQ

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