solidrust / Phi-3-mini-4k-instruct-AWQ

huggingface.co
Total runs: 98
24-hour runs: -2
7-day runs: -33
30-day runs: -235
Model's Last Updated: September 03 2024
text-generation

Introduction of Phi-3-mini-4k-instruct-AWQ

Model Details of Phi-3-mini-4k-instruct-AWQ

microsoft/Phi-3-mini-4k-instruct 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/Phi-3-mini-4k-instruct-AWQ"
system_message = "You are Phi-3-mini-4k-instruct, incarnated as a powerful AI. You were created by microsoft."

# 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 Phi-3-mini-4k-instruct-AWQ on huggingface.co

98
Total runs
-2
24-hour runs
-11
3-day runs
-33
7-day runs
-235
30-day runs

More Information About Phi-3-mini-4k-instruct-AWQ huggingface.co Model

Phi-3-mini-4k-instruct-AWQ huggingface.co

Phi-3-mini-4k-instruct-AWQ huggingface.co is an AI model on huggingface.co that provides Phi-3-mini-4k-instruct-AWQ's model effect (), which can be used instantly with this solidrust Phi-3-mini-4k-instruct-AWQ model. huggingface.co supports a free trial of the Phi-3-mini-4k-instruct-AWQ model, and also provides paid use of the Phi-3-mini-4k-instruct-AWQ. Support call Phi-3-mini-4k-instruct-AWQ model through api, including Node.js, Python, http.

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https://huggingface.co/solidrust/Phi-3-mini-4k-instruct-AWQ

solidrust Phi-3-mini-4k-instruct-AWQ online free

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https://huggingface.co/solidrust/Phi-3-mini-4k-instruct-AWQ

Phi-3-mini-4k-instruct-AWQ install

Phi-3-mini-4k-instruct-AWQ is an open source model from GitHub that offers a free installation service, and any user can find Phi-3-mini-4k-instruct-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of Phi-3-mini-4k-instruct-AWQ install, users can directly use Phi-3-mini-4k-instruct-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/solidrust/Phi-3-mini-4k-instruct-AWQ

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