solidrust / flammen23X-mistral-7B-AWQ

huggingface.co
Total runs: 24
24-hour runs: 1
7-day runs: 3
30-day runs: 7
Model's Last Updated: September 03 2024
text-generation

Introduction of flammen23X-mistral-7B-AWQ

Model Details of flammen23X-mistral-7B-AWQ

flammenai/flammen23X-mistral-7B AWQ

image/png

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/flammen23X-mistral-7B-AWQ"
system_message = "You are flammen23X-mistral-7B, incarnated as a powerful AI. You were created by flammenai."

# 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 flammen23X-mistral-7B-AWQ on huggingface.co

24
Total runs
1
24-hour runs
4
3-day runs
3
7-day runs
7
30-day runs

More Information About flammen23X-mistral-7B-AWQ huggingface.co Model

More flammen23X-mistral-7B-AWQ license Visit here:

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

flammen23X-mistral-7B-AWQ huggingface.co

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flammen23X-mistral-7B-AWQ huggingface.co Url

https://huggingface.co/solidrust/flammen23X-mistral-7B-AWQ

solidrust flammen23X-mistral-7B-AWQ online free

flammen23X-mistral-7B-AWQ huggingface.co is an online trial and call api platform, which integrates flammen23X-mistral-7B-AWQ's modeling effects, including api services, and provides a free online trial of flammen23X-mistral-7B-AWQ, you can try flammen23X-mistral-7B-AWQ online for free by clicking the link below.

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https://huggingface.co/solidrust/flammen23X-mistral-7B-AWQ

flammen23X-mistral-7B-AWQ install

flammen23X-mistral-7B-AWQ is an open source model from GitHub that offers a free installation service, and any user can find flammen23X-mistral-7B-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of flammen23X-mistral-7B-AWQ install, users can directly use flammen23X-mistral-7B-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

flammen23X-mistral-7B-AWQ install url in huggingface.co:

https://huggingface.co/solidrust/flammen23X-mistral-7B-AWQ

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