granite-3.1-2b-instruct-quantized.w4a16

Model Overview

• Model Architecture: granite-3.1-2b-instruct
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT4
  • Activation quantization: INT4
• Release Date: 1/8/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of ibm-granite/granite-3.1-2b-instruct. It achieves an average score of 61.54 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 61.98.

Model Optimizations

This model was obtained by quantizing the weights of ibm-granite/granite-3.1-2b-instruct to INT4 data type, ready for inference with vLLM >= 0.5.2. 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.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/granite-3.1-2b-instruct-quantized.w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])

messages_list = [
    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]

outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below.

Model Creation Code

python quantize.py --model_path ibm-granite/granite-3.1-2b-instruct --quant_path "output_dir/granite-3.1-2b-instruct-quantized.w4a16" --calib_size 1024 --dampening_frac 0.01 --observer mse --group_size 64

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot, apply
import argparse
from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy


parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--quant_path', type=str)
parser.add_argument('--calib_size', type=int, default=256)
parser.add_argument('--dampening_frac', type=float, default=0.1) 
parser.add_argument('--observer', type=str, default="minmax")
parser.add_argument('--group_size', type=int, default="128")
args = parser.parse_args()

model = AutoModelForCausalLM.from_pretrained(
    args.model_path,
    device_map="auto",
    torch_dtype="auto",
    use_cache=False,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_path)


NUM_CALIBRATION_SAMPLES = args.calib_size
DATASET_ID = "neuralmagic/LLM_compression_calibration"
DATASET_SPLIT = "train"
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))

def preprocess(example):
    concat_txt = example["instruction"] + "\n" + example["output"]
    return {"text": concat_txt}

ds = ds.map(preprocess)

def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        truncation=False,
        add_special_tokens=True,
    )


ds = ds.map(tokenize, remove_columns=ds.column_names)

recipe = [
    GPTQModifier(
        targets=["Linear"],
        ignore=["lm_head"],
        scheme="w4a16",
        dampening_frac=args.dampening_frac,
        observer=args.observer,
        group_size=args.group_size
    )
]
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    num_calibration_samples=args.calib_size,
    max_seq_length=8196,
)

# Save to disk compressed.
model.save_pretrained(quant_path, save_compressed=True)
tokenizer.save_pretrained(quant_path)

Evaluation

The model was evaluated on OpenLLM Leaderboard V1, OpenLLM Leaderboard V2 and on HumanEval, using the following commands:

Evaluation Commands

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/granite-3.1-2b-instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --tasks openllm \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

OpenLLM Leaderboard V2:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/granite-3.1-2b-instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --tasks leaderboard \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

HumanEval

Generation

python3 codegen/generate.py \
  --model neuralmagic/granite-3.1-2b-instruct-quantized.w4a16 \
  --bs 16 \
  --temperature 0.2 \
  --n_samples 50 \
  --root "." \
  --dataset humaneval

Sanitization

python3 evalplus/sanitize.py \
  humaneval/neuralmagic--granite-3.1-2b-instruct-quantized.w4a16_vllm_temp_0.2

Evaluation

evalplus.evaluate \
  --dataset humaneval \
  --samples humaneval/neuralmagic--granite-3.1-2b-instruct-quantized.w4a16_vllm_temp_0.2-sanitized

Accuracy

Category	Metric	ibm-granite/granite-3.1-2b-instruct	neuralmagic/granite-3.1-2b-instruct-quantized.w4a16	Recovery (%)
OpenLLM v1	ARC-Challenge (Acc-Norm, 25-shot)	55.63	54.18	97.39
	GSM8K (Strict-Match, 5-shot)	60.96	62.85	103.10
	HellaSwag (Acc-Norm, 10-shot)	75.21	73.36	97.54
	MMLU (Acc, 5-shot)	54.38	52.17	95.93
	TruthfulQA (MC2, 0-shot)	55.93	56.83	101.61
	Winogrande (Acc, 5-shot)	69.67	69.85	100.26
	Average Score	61.98	61.54	99.29
OpenLLM v2	IFEval (Inst Level Strict Acc, 0-shot)	67.99	67.63	99.47
	BBH (Acc-Norm, 3-shot)	44.11	43.22	97.98
	Math-Hard (Exact-Match, 4-shot)	8.66	8.77	101.27
	GPQA (Acc-Norm, 0-shot)	28.30	28.56	100.92
	MUSR (Acc-Norm, 0-shot)	35.12	35.26	100.40
	MMLU-Pro (Acc, 5-shot)	26.87	27.27	101.49
	Average Score	35.17	35.12	99.84
HumanEval	HumanEval Pass@1	53.40	52.30	97.94

Inference Performance

This model achieves up to 1.9x speedup in single-stream deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with vLLM version 0.6.6.post1, and GuideLLM.

Benchmarking Command

guidellm --model neuralmagic/granite-3.1-2b-instruct-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server

Single-stream performance (measured with vLLM version 0.6.6.post1)

			Latency (s)
GPU class	Model	Speedup	Code Completion prefill: 256 tokens decode: 1024 tokens	Docstring Generation prefill: 768 tokens decode: 128 tokens	Code Fixing prefill: 1024 tokens decode: 1024 tokens	RAG prefill: 1024 tokens decode: 128 tokens	Instruction Following prefill: 256 tokens decode: 128 tokens	Multi-turn Chat prefill: 512 tokens decode: 256 tokens	Large Summarization prefill: 4096 tokens decode: 512 tokens
A5000	granite-3.1-2b-instruct		10.9	1.4	11.0	1.5	1.4	2.8	6.1
	granite-3.1-2b-instruct-quantized.w8a8	1.37	7.9	1.0	8.0	1.1	1.0	2.0	4.7
	granite-3.1-2b-instruct-quantized.w4a16 (this model)	1.94	5.4	0.7	5.5	0.8	0.7	1.4	3.4
A6000	granite-3.1-2b-instruct		9.8	1.3	10.0	1.3	1.3	2.6	5.4
	granite-3.1-2b-instruct-quantized.w8a8	1.31	7.8	1.0	7.6	1.0	0.9	1.9	4.5
	granite-3.1-2b-instruct-quantized.w4a16 (this model)	1.87	5.1	0.7	5.2	0.7	0.7	1.3	3.1
L40	granite-3.1-2b-instruct		9.3	1.2	9.4	1.2	1.2	2.3	5.0
	granite-3.1-2b-instruct-FP8-dynamic	1.26	7.3	0.9	7.4	1.0	0.9	1.8	4.1
	granite-3.1-2b-instruct-quantized.w4a16 (this model)	1.88	4.8	0.6	4.9	0.6	0.6	1.2	2.8