Orpheus 3b 0.1 Ft.w8a8

🚀 Transformers Library for Text-to-Speech

This library based on the 'transformers' provides a text-to-speech solution, leveraging the 'canopylabs/orpheus-3b-0.1-ft' base model.

🚀 Quick Start

Prerequisites

• The library uses the transformers for model handling, datasets for loading calibration data, and llmcompressor for model compression.

Creation Process

The following code demonstrates how to create and compress a model using the provided library.

from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier

model_id  = "canopylabs/orpheus-3b-0.1-ft"
model_out = "orpheus-3b-0.1-ft.w8a8"

num_samples = 64
max_seq_len = 4096

tokenizer = AutoTokenizer.from_pretrained(model_id)

def preprocess_fn(example):
  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}

ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.shuffle().select(range(num_samples))
ds = ds.map(preprocess_fn)

recipe = [
  SmoothQuantModifier(
    smoothing_strength=0.7,
    mappings=[
      [["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
      [["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
      [["re:.*down_proj"], "re:.*up_proj"],
    ],
  ),
  GPTQModifier(
    sequential=True,
    targets="Linear",
    scheme="W8A8",
    ignore=["lm_head"],
    dampening_frac=0.01,
  )
]

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  device_map="auto",
  torch_dtype="bfloat16",
)

oneshot(
  model=model,
  dataset=ds,
  recipe=recipe,
  max_seq_length=max_seq_len,
  num_calibration_samples=num_samples,
)

model.save_pretrained(model_out)

📄 License

This project is licensed under the Apache-2.0 license.