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RedHatAI/
granite-3.1-8b-instruct-quantized.w8a8

Name: granite-3.1-8b-instruct-quantized.w8a8
Availability: InStock

text-generation-inference

text-generation

ibm-granite/granite-3.1-8b-instruct

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granite-3.1-8b-instruct-quantized.w8a8
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Model Overview

Model Architecture: granite-3.1-8b-instruct
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: INT8
- Activation quantization: INT8
Release Date: 1/8/2025
Version: 1.0
Validated on: RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
Model Developers: Neural Magic

Quantized version of ibm-granite/granite-3.1-8b-instruct. It achieves an average score of 70.26 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 70.30.

Model Optimizations

This model was obtained by quantizing the weights and activations of ibm-granite/granite-3.1-8b-instruct to INT8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations 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.

python

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/granite-3.1-8b-instruct-quantized.w8a8"
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.

Deploy on Red Hat AI Inference Server

bash

podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
 --ipc=host \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
--name=vllm \
registry.access.redhat.com/rhaiis/rh-vllm-cuda \
vllm serve \
--tensor-parallel-size 8 \
--max-model-len 32768  \
--enforce-eager --model RedHatAI/granite-3.1-8b-instruct-quantized.w8a8

See Red Hat AI Inference Server documentation for more details.

Deploy on Red Hat Enterprise Linux AI

bash

# Download model from Red Hat Registry via docker
# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
ilab model download --repository docker://registry.redhat.io/rhelai1/granite-3-1-8b-instruct-quantized-w8a8:1.5

bash

# Serve model via ilab
ilab model serve --model-path ~/.cache/instructlab/models/granite-3-1-8b-instruct-quantized-w8a8 -- --trust-remote-code
  
# Chat with model
ilab model chat --model ~/.cache/instructlab/models/granite-3-1-8b-instruct-quantized-w8a8

See Red Hat Enterprise Linux AI documentation for more details.

Deploy on Red Hat Openshift AI

python

# Setting up vllm server with ServingRuntime
# Save as: vllm-servingruntime.yaml
apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
 name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
 annotations:
   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
 labels:
   opendatahub.io/dashboard: 'true'
spec:
 annotations:
   prometheus.io/port: '8080'
   prometheus.io/path: '/metrics'
 multiModel: false
 supportedModelFormats:
   - autoSelect: true
     name: vLLM
 containers:
   - name: kserve-container
     image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm
     command:
       - python
       - -m
       - vllm.entrypoints.openai.api_server
     args:
       - "--port=8080"
       - "--model=/mnt/models"
       - "--served-model-name={{.Name}}"
     env:
       - name: HF_HOME
         value: /tmp/hf_home
     ports:
       - containerPort: 8080
         protocol: TCP

python

# Attach model to vllm server. This is an NVIDIA template
# Save as: inferenceservice.yaml
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  annotations:
    openshift.io/display-name: granite-3-1-8b-instruct-quantized-w8a8 # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: granite-3-1-8b-instruct-quantized-w8a8          # specify model name. This value will be used to invoke the model in the payload
  labels:
    opendatahub.io/dashboard: 'true'
spec:
  predictor:
    maxReplicas: 1
    minReplicas: 1
    model:
      args:
        - '--trust-remote-code'
      modelFormat:
        name: vLLM
      name: ''
      resources:
        limits:
          cpu: '2'			# this is model specific
          memory: 8Gi		# this is model specific
          nvidia.com/gpu: '1'	# this is accelerator specific
        requests:			# same comment for this block
          cpu: '1'
          memory: 4Gi
          nvidia.com/gpu: '1'
      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
      storageUri: oci://registry.redhat.io/rhelai1/modelcar-granite-3-1-8b-instruct-quantized-w8a8:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists

bash

# make sure first to be in the project where you want to deploy the model
# oc project <project-name>

# apply both resources to run model

# Apply the ServingRuntime
oc apply -f vllm-servingruntime.yaml

# Apply the InferenceService
oc apply -f qwen-inferenceservice.yaml

python

# Replace <inference-service-name> and <cluster-ingress-domain> below:
# - Run `oc get inferenceservice` to find your URL if unsure.

# Call the server using curl:
curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
        -H "Content-Type: application/json" \
        -d '{
    "model": "granite-3-1-8b-instruct-quantized-w8a8",
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "max_tokens": 1,
    "messages": [
        {
            "role": "user",
            "content": "How can a bee fly when its wings are so small?"
        }
    ]
}'

See Red Hat Openshift AI 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-3.1-8b-instruct --quant_path "output_dir/granite-3.1-8b-instruct-quantized.w8a8" --calib_size 3072 --dampening_frac 0.1 --observer mse

python

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
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")
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):
    return {"text": example["text"]}

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)

ignore=["lm_head"]
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"]
]

recipe = [
    SmoothQuantModifier(smoothing_strength=0.8, ignore=ignore, mappings=mappings),
    GPTQModifier(
        targets=["Linear"],
        ignore=["lm_head"],
        scheme="W8A8",
        dampening_frac=args.dampening_frac,
        observer=args.observer,
    )
]
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-3.1-8b-instruct-quantized.w8a8",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-3.1-8b-instruct-quantized.w8a8",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-3.1-8b-instruct-quantized.w8a8 \
  --bs 16 \
  --temperature 0.2 \
  --n_samples 50 \
  --root "." \
  --dataset humaneval

Sanitization

python3 evalplus/sanitize.py \
  humaneval/neuralmagic--granite-3.1-8b-instruct-quantized.w8a8_vllm_temp_0.2

Evaluation

evalplus.evaluate \
  --dataset humaneval \
  --samples humaneval/neuralmagic--granite-3.1-8b-instruct-quantized.w8a8_vllm_temp_0.2-sanitized

Accuracy

Category	Metric	ibm-granite/granite-3.1-8b-instruct	neuralmagic/granite-3.1-8b-instruct-quantized.w8a8	Recovery (%)
OpenLLM V1	ARC-Challenge (Acc-Norm, 25-shot)	66.81	67.06	100.37
GSM8K (Strict-Match, 5-shot)	64.52	65.66	101.77
HellaSwag (Acc-Norm, 10-shot)	84.18	83.93	99.70
MMLU (Acc, 5-shot)	65.52	65.03	99.25
TruthfulQA (MC2, 0-shot)	60.57	60.02	99.09
Winogrande (Acc, 5-shot)	80.19	79.87	99.60
Average Score	70.30	70.26	99.95
OpenLLM V2	IFEval (Inst Level Strict Acc, 0-shot)	74.01	73.50	99.31
BBH (Acc-Norm, 3-shot)	53.19	52.59	98.87
Math-Hard (Exact-Match, 4-shot)	14.77	15.73	106.50
GPQA (Acc-Norm, 0-shot)	31.76	30.62	96.40
MUSR (Acc-Norm, 0-shot)	46.01	44.30	96.28
MMLU-Pro (Acc, 5-shot)	35.81	35.41	98.88
Average Score	42.61	42.03	98.64
Coding	HumanEval Pass@1	71.00	70.50	99.30

Inference Performance

This model achieves up to 1.6x speedup in single-stream deployment and up to 1.7x speedup in multi-stream asynchronous 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-3.1-8b-instruct-quantized.w8a8 --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)

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
			Latency (s)
A5000	granite-3.1-8b-instruct		28.3	3.7	28.8	3.8	3.6	7.2	15.7
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.60	17.7	2.3	18.0	2.4	2.2	4.5	10.0
granite-3.1-8b-instruct-quantized.w4a16	2.61	10.3	1.5	10.7	1.5	1.3	2.7	6.6
A6000	granite-3.1-8b-instruct		25.8	3.4	26.2	3.4	3.3	6.5	14.2
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.50	17.4	2.3	16.9	2.2	2.2	4.4	9.8
granite-3.1-8b-instruct-quantized.w4a16	2.48	10.0	1.4	10.4	1.5	1.3	2.5	6.2
A100	granite-3.1-8b-instruct		13.6	1.8	13.7	1.8	1.7	3.4	7.3
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.31	10.4	1.3	10.5	1.4	1.3	2.6	5.6
granite-3.1-8b-instruct-quantized.w4a16	1.80	7.3	1.0	7.4	1.0	0.9	1.9	4.3

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

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
			Maximum Throughput (Queries per Second)
A5000	granite-3.1-8b-instruct		0.8	3.1	0.4	2.5	6.7	2.7	0.3
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.71	1.3	5.2	0.9	4.0	10.5	4.4	0.5
granite-3.1-8b-instruct-quantized.w4a16	1.46	1.3	3.9	0.8	2.9	8.2	3.6	0.5
A6000	granite-3.1-8b-instruct		1.3	5.1	0.9	4.0	0.3	4.3	0.6
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.39	1.8	7.0	1.3	5.6	14.0	6.3	0.8
granite-3.1-8b-instruct-quantized.w4a16	1.09	1.9	4.8	1.0	3.8	10.0	5.0	0.6
A100	granite-3.1-8b-instruct		3.1	10.7	2.1	8.5	20.6	9.6	1.4
granite-3.1-8b-instruct-quantized.w8a8 (this model)	1.23	3.8	14.2	2.1	11.4	25.9	12.1	1.7
granite-3.1-8b-instruct-quantized.w4a16	0.96	3.4	9.0	2.6	7.2	18.0	8.8	1.3

RedHatAI/granite-3.1-8b-instruct-quantized.w8a8

granite-3.1-8b-instruct-quantized.w8a8 Model Icon

Model Overview

Model Optimizations

Deployment

Use with vLLM

Creation

Evaluation

HumanEval

Generation

Sanitization

Evaluation

Accuracy

Inference Performance

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

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

RedHatAI/
granite-3.1-8b-instruct-quantized.w8a8

granite-3.1-8b-instruct-quantized.w8a8
Model Icon