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solidrust/BeyondInfinity-v2-4x7B-AWQ
BeyondInfinity-v2-4x7B-AWQ is a text generation model from solidrust. Use it when you need the model to write or continue text. It is set up for transformers.
- Model creator: R136a1 - Original model: BeyondInfinity-v2-4x7B
Downloads · 30 days
13
5% of all-time downloads
All-time downloads
264
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24.2B
12.9 GB on disk
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1
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.safetensors12.9 GB · 100%
How the weights are stored.
I3223.9B · 99%
From the Hugging Face model README
pip install --upgrade autoawq autoawq-kernels
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/BeyondInfinity-v2-4x7B-AWQ"
system_message = "You are BeyondInfinity-v2-4x7B, incarnated as a powerful AI. You were created by R136a1."
# 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)
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: