solidrust / Mistral7B-PairRM-SPPO-Iter2-AWQ

huggingface.co
Total runs: 24
24-hour runs: -1
7-day runs: 7
30-day runs: 11
Model's Last Updated: September 03 2024
text-generation

Introduction of Mistral7B-PairRM-SPPO-Iter2-AWQ

Model Details of Mistral7B-PairRM-SPPO-Iter2-AWQ

UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 AWQ

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/Mistral7B-PairRM-SPPO-Iter2-AWQ"
system_message = "You are Mistral7B-PairRM-SPPO-Iter2, incarnated as a powerful AI. You were created by UCLA-AGI."

# 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 Mistral7B-PairRM-SPPO-Iter2-AWQ on huggingface.co

24
Total runs
-1
24-hour runs
2
3-day runs
7
7-day runs
11
30-day runs

More Information About Mistral7B-PairRM-SPPO-Iter2-AWQ huggingface.co Model

Mistral7B-PairRM-SPPO-Iter2-AWQ huggingface.co

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Mistral7B-PairRM-SPPO-Iter2-AWQ install

Mistral7B-PairRM-SPPO-Iter2-AWQ is an open source model from GitHub that offers a free installation service, and any user can find Mistral7B-PairRM-SPPO-Iter2-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of Mistral7B-PairRM-SPPO-Iter2-AWQ install, users can directly use Mistral7B-PairRM-SPPO-Iter2-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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