metadata

license: mit
pipeline_tag: text-generation

Intro

Activation Beacon is a plug-in module to transformer-based LLMs that enables effective, efficient, and flexible compression of long contexts.

Environment

pip install transformers
pip install flash-attn --no-build-isolation

Usage

import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "namespace-Pt/beacon-qwen-2-7b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True, 
    torch_dtype=torch.bfloat16, 
    attn_implementation="flash_attention_2"
)

model = model.cuda().eval()

with torch.no_grad():
  # short context
  messages = [{"role": "user", "content": "Tell me about yourself."}]
  inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
  outputs = model.generate(**inputs, max_new_tokens=50)
  print(f"Input Length: {inputs['input_ids'].shape[1]}")
  print(f"Output:       {repr(tokenizer.decode(outputs[0], skip_special_tokens=True))}")

  # reset memory before new generation task
  model.memory.reset()

  # long context
  with open("infbench.json", encoding="utf-8") as f:
    example = json.load(f)
  messages = [{"role": "user", "content": example["context"]}]
  inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
  outputs = model.generate(**inputs, do_sample=False, top_p=1, temperature=1, max_new_tokens=20)[:, inputs["input_ids"].shape[1]:]
  print("*"*20)
  print(f"Input Length: {inputs['input_ids'].shape[1]}")
  print(f"Answers:      {example['answer']}")
  print(f"Prediction:   {tokenizer.decode(outputs[0], skip_special_tokens=True)}")

NOTE: It's okay to see warnings like This is a friendly reminder - the current text generation call will exceed the model's predefined maximum length (32768). Depending on the model, you may observe exceptions, performance degradation, or nothing at all. Just ignore it.