license: llama2
train: false
inference: false
pipeline_tag: text-generation
This is an experimental HQQ 1-bit quantized (binary weights) Llama2-7B-chat model using a LoRA adapter to improve the performance (referred to as HQQ+).
Quantizing small models at extreme low-bits is a challenging task. The purpose of this model is to show the community what to expect when fine-tuning such models. We notice that, 1-bit quantization doesn't work well when applied directly on small models such as the Llama2-7B. However, when fine-tuned, the model's ouput significantly improves. In fact, the 1-bit base model outperforms Quip# 2-bit after fine-tuning on ~2.8K samples.
Note that the weights here are unsigned 1-bit (0 or 1), not ternary like the recent 1.58-bit work . This is a more challenging task since we lose the sign of the weights and only fine-tune a small fraction of the parameters (~94MB worth of weights). The dequantization step can be rewriten as a 1-bit matmul which could potentially require only additions + a very low-rank matmul which is fast to compute.
Datasets
The adapter was trained via SFT on random subsets of the following:
Base Model
- wikitext-2-raw-v1 (full)
Chat Model
- timdettmers/openassistant-guanaco (full)
- microsoft/orca-math-word-problems-200k (25K)
- meta-math/MetaMathQA (25K)
- HuggingFaceH4/ultrafeedback_binarized (25K - chosen answers only)
Performance
| Models | Llama2-7B (fp16) | Llama2-7B (HQQ-1bit) | Llama2-7B (HQQ+-1bit) | Quip# (2bit) |
|---|---|---|---|---|
| Wiki Perpexlity | 5.18 | 9866 | 8.53 | 8.54 |
| VRAM (GB) | 13.5 | 1.76 | 1.85 | 2.72 |
| forward time (sec) | 0.1 | 0.231 | 0.257 | 0.353 |
| Models | Llama2-7B-chat (fp16) | Llama2-7B-chat (HQQ-1bit) | Llama2-7B-chat (HQQ+-1bit) |
|---|---|---|---|
| ARC (25-shot) | 53.67 | 21.59 | 31.14 |
| HellaSwag (10-shot) | 78.56 | 25.66 | 52.96 |
| MMLU (5-shot) | 48.16 | 25.08 | 26.54 |
| TruthfulQA-MC2 | 45.32 | 47.81 | 43.16 |
| Winogrande (5-shot) | 72.53 | 49.72 | 60.54 |
| GSM8K (5-shot) | 23.12 | 0 | 11 |
| Average | 53.56 | 28.31 | 37.56 |
Usage
First, install the latest version of HQQ:
pip install git+https://github.com/mobiusml/hqq.git
Then you can use the sample code below:
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
#Load the model
model_id = 'mobiuslabsgmbh/Llama-2-7b-chat-hf_1bitgs8_hqq'
model = HQQModelForCausalLM.from_quantized(model_id, adapter='adapter_v0.1.lora')
tokenizer = AutoTokenizer.from_pretrained(model_id)
#Setup Inference Mode
tokenizer.add_bos_token = False
tokenizer.add_eos_token = False
if not tokenizer.pad_token: tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.config.use_cache = True
model.eval();
# Optional: torch compile for faster inference
# model = torch.compile(model)
#Streaming Inference
import torch, transformers
from threading import Thread
def chat_processor(chat, max_new_tokens=100, do_sample=True, device='cuda'):
tokenizer.use_default_system_prompt = False
streamer = transformers.TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_params = dict(
tokenizer("<s> [INST] " + chat + " [/INST] ", return_tensors="pt").to(device),
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
pad_token_id=tokenizer.pad_token_id,
top_p=0.90 if do_sample else None,
top_k=50 if do_sample else None,
temperature= 0.6 if do_sample else None,
num_beams=1,
repetition_penalty=1.2,
)
t = Thread(target=model.generate, kwargs=generate_params)
t.start()
print("User: ", chat);
print("Assistant: ");
outputs = ""
for text in streamer:
outputs += text
print(text, end="", flush=True)
torch.cuda.empty_cache()
return outputs
Example
outputs = chat_processor("What is the solution to x^2 - 1 = 0", max_new_tokens=1000, do_sample=False)
User: What is the solution to x^2 - 1 = 0
Assistant:
The equation $x^2 - 1 = 0$ can be factored as $(x-1)(x+1) = 0$.
You want to find a value of $x$ that makes this true for all values of $x$. This means that either $x=1$ or $-1$, or $x=-1$. So, there are two solutions: $x=\boxed{1}$ and $x=\boxed{-1}$. The answer is: 1