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nm-testing/
SmolLM-1.7B-Instruct-quantized.w4a16

Name: SmolLM-1.7B-Instruct-quantized.w4a16
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SmolLM-135M-Instruct-quantized.w4a16

Model Overview

Model Architecture: SmolLM-135M-Instruct
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: INT4
Intended Use Cases: Intended for commercial and research use in English. Similarly to SmolLM-135M-Instruct, this models is intended for assistant-like chat.
Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
Release Date: 8/23/2024
Version: 1.0
License(s): Apache-2.0
Model Developers: Neural Magic

Quantized version of SmolLM-135M-Instruct. It achieves an average score of 31.91 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 31.55.

Model Optimizations

This model was obtained by quantizing the weights of SmolLM-135M-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within transformers blocks are quantized. Symmetric group-wise quantization is applied, in which a linear scaling per group maps the INT4 and floating point representations of the quantized weights. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. Quantization is performed with 10% damping factor, group-size as 64 and 512 sequences sampled from LLM Compression Calibration.

Creation

This model was created by using the llm-compressor library as presented in the code snipet below.

python

from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy
from datasets import load_dataset
import random

model_id = "HuggingFaceTB/SmolLM-135M-Instruct"


num_samples = 512
max_seq_len = 4096

tokenizer = AutoTokenizer.from_pretrained(model_id)

preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)}

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

examples = [
    tokenizer(
        example["text"], padding=False, max_length=max_seq_len, truncation=True,
    ) for example in ds
]

# recipe = "w4a16_nohead_recipe.yaml"
recipe = GPTQModifier(
  targets="Linear",
  scheme="W4A16",
  ignore=["lm_head"],
  dampening_frac=0.1,
)


model = SparseAutoModelForCausalLM.from_pretrained(
  model_id,
  device_map="auto",
  trust_remote_code=True
)

print(model)

oneshot(
  model=model,
  dataset=ds,
  recipe=recipe,
  max_seq_length=max_seq_len,
  num_calibration_samples=num_samples,
  oneshot_device="cuda:1,2,3",
)

model_name = model_id.split("/")[-1]

model.save_pretrained(f"{model_name}-quantized.w4a16")
tokenizer.save_pretrained(f"{model_name}-quantized.w4a16")

Evaluation

The model was evaluated on the OpenLLM leaderboard tasks (version 1) with the lm-evaluation-harness (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the sparseML engine, using the following command:

lm_eval \
  --model sparseml \
  --model_args pretrained=nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True \
  --tasks openllm \
  --batch_size auto

Accuracy

Open LLM Leaderboard evaluation scores

Benchmark	SmolLM-135M-Instruct	SmolLM-135M-Instruct-quantized.w4a16(this model)	Recovery
MMLU (5-shot)	26.220	25.202	96.12%
ARC Challenge (25-shot)	29.948	30.034	100.29%
GSM-8K (5-shot, strict-match)	1.289	1.971	152.91%
Hellaswag (10-shot)	41.41	40.81	98.55%
Winogrande (5-shot)	50.039	53.591	107.10%
TruthfulQA (0-shot)	40.38	39.87	98.74%
Average	31.55	31.91	101.16%