Instructions to use RedHatAI/granite-3.1-2b-base-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/granite-3.1-2b-base-quantized.w8a8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RedHatAI/granite-3.1-2b-base-quantized.w8a8")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/granite-3.1-2b-base-quantized.w8a8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/granite-3.1-2b-base-quantized.w8a8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/granite-3.1-2b-base-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/granite-3.1-2b-base-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/granite-3.1-2b-base-quantized.w8a8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/granite-3.1-2b-base-quantized.w8a8

SGLang

How to use RedHatAI/granite-3.1-2b-base-quantized.w8a8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RedHatAI/granite-3.1-2b-base-quantized.w8a8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/granite-3.1-2b-base-quantized.w8a8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RedHatAI/granite-3.1-2b-base-quantized.w8a8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/granite-3.1-2b-base-quantized.w8a8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/granite-3.1-2b-base-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/granite-3.1-2b-base-quantized.w8a8
```

granite-3.1-2b-base-quantized.w8a8 / README.md

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	---
	tags:
	- w8a8
	- int8
	- vllm
	license: apache-2.0
	license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
	language:
	- en
	base_model: ibm-granite/granite-3.1-2b-base
	library_name: transformers
	---

	# granite-3.1-2b-base-quantized.w8a8

	## Model Overview
	- Model Architecture: granite-3.1-2b-base
	- Input: Text
	- Output: Text
	- Model Optimizations:
	- Weight quantization: INT8
	- Activation quantization: INT8
	- Release Date: 1/8/2025
	- Version: 1.0
	- Model Developers: Neural Magic

	Quantized version of [ibm-granite/granite-3.1-2b-base](https://huggingface.co/ibm-granite/granite-3.1-2b-base).
	It achieves an average score of 57.22 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 57.65.

	### Model Optimizations

	This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-2b-base](https://huggingface.co/ibm-granite/granite-3.1-2b-base) 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](https://docs.vllm.ai/en/latest/) 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-3.1-2b-base-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](https://docs.vllm.ai/en/latest/) for more details.

	## Creation

	This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.

	<details>
	<summary>Model Creation Code</summary>

	```bash
	python quantize.py --model_path ibm-granite/granite-3.1-2b-base --quant_path "output_dir/granite-3.1-2b-base-quantized.w8a8" --calib_size 1024 --dampening_frac 0.01 --observer mse
	```


	```python
	from datasets import load_dataset
	from transformers import AutoTokenizer
	from llmcompressor.modifiers.quantization import GPTQModifier
	from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
	from llmcompressor.transformers import SparseAutoModelForCausalLM, 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 = SparseAutoModelForCausalLM.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=0.7, 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)
	```
	</details>

	## Evaluation

	The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:

	<details>
	<summary>Evaluation Commands</summary>

	OpenLLM Leaderboard V1:
	```
	lm_eval \
	--model vllm \
	--model_args pretrained="neuralmagic/granite-3.1-2b-base-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
	```

	#### HumanEval
	##### Generation
	```
	python3 codegen/generate.py \
	--model neuralmagic/granite-3.1-2b-base-quantized.w8a8 \
	--bs 16 \
	--temperature 0.2 \
	--n_samples 50 \
	--root "." \
	--dataset humaneval
	```
	##### Sanitization
	```
	python3 evalplus/sanitize.py \
	humaneval/neuralmagic--granite-3.1-2b-base-quantized.w8a8_vllm_temp_0.2
	```
	##### Evaluation
	```
	evalplus.evaluate \
	--dataset humaneval \
	--samples humaneval/neuralmagic--granite-3.1-2b-base-quantized.w8a8_vllm_temp_0.2-sanitized
	```
	</details>

	### Accuracy

	<table>
	<thead>
	<tr>
	<th>Category</th>
	<th>Metric</th>
	<th>ibm-granite/granite-3.1-2b-base</th>
	<th>neuralmagic/granite-3.1-2b-base-quantized.w8a8</th>
	<th>Recovery (%)</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td rowspan="7"><b>OpenLLM V1</b></td>
	<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
	<td>53.75</td>
	<td>54.01</td>
	<td>100.48</td>
	</tr>
	<tr>
	<td>GSM8K (Strict-Match, 5-shot)</td>
	<td>47.84</td>
	<td>46.55</td>
	<td>97.30</td>
	</tr>
	<tr>
	<td>HellaSwag (Acc-Norm, 10-shot)</td>
	<td>77.94</td>
	<td>77.94</td>
	<td>100.00</td>
	</tr>
	<tr>
	<td>MMLU (Acc, 5-shot)</td>
	<td>52.88</td>
	<td>52.34</td>
	<td>98.98</td>
	</tr>
	<tr>
	<td>TruthfulQA (MC2, 0-shot)</td>
	<td>39.04</td>
	<td>38.12</td>
	<td>97.64</td>
	</tr>
	<tr>
	<td>Winogrande (Acc, 5-shot)</td>
	<td>74.43</td>
	<td>74.35</td>
	<td>99.89</td>
	</tr>
	<tr>
	<td><b>Average Score</b></td>
	<td><b>57.65</b></td>
	<td><b>57.22</b></td>
	<td><b>99.26</b></td>
	</tr>
	<tr>
	<td rowspan="2"><b>Coding</b></td>
	<td>HumanEval Pass@1</td>
	<td>30.00</td>
	<td>29.60</td>
	<td><b>98.67</b></td>
	</tr>
	</tbody>
	</table>



	## Inference Performance


	This model achieves up to 1.4x speedup in single-stream deployment and up to 1.1x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
	The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm).

	<details>
	<summary>Benchmarking Command</summary>

	```
	guidellm --model neuralmagic/granite-3.1-2b-base-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
	```

	</details>


	### Single-stream performance (measured with vLLM version 0.6.6.post1)
	<table>
	<tr>
	<td></td>
	<td></td>
	<td></td>
	<th style="text-align: center;" colspan="7" >Latency (s)</th>
	</tr>
	<tr>
	<th>GPU class</th>
	<th>Model</th>
	<th>Speedup</th>
	<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
	<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
	<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
	<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
	<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
	<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
	<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
	</tr>
	<tr>
	<td style="vertical-align: middle;" rowspan="3" >A5000</td>
	<td>granite-3.1-2b-base</td>
	<td></td>
	<td>10.9</td>
	<td>1.4</td>
	<td>11.0</td>
	<td>1.5</td>
	<td>1.4</td>
	<td>2.8</td>
	<td>6.1</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
	<td>1.37</td>
	<td>7.9</td>
	<td>1.0</td>
	<td>8.0</td>
	<td>1.1</td>
	<td>1.0</td>
	<td>2.0</td>
	<td>4.7</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w4a16</td>
	<td>1.94</td>
	<td>5.4</td>
	<td>0.7</td>
	<td>5.5</td>
	<td>0.8</td>
	<td>0.7</td>
	<td>1.4</td>
	<td>3.4</td>
	</tr>
	<tr>
	<td style="vertical-align: middle;" rowspan="3" >A6000</td>
	<td>granite-3.1-2b-base</td>
	<td></td>
	<td>9.8</td>
	<td>1.3</td>
	<td>10.0</td>
	<td>1.3</td>
	<td>1.3</td>
	<td>2.6</td>
	<td>5.4</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
	<td>1.31</td>
	<td>7.8</td>
	<td>1.0</td>
	<td>7.6</td>
	<td>1.0</td>
	<td>0.9</td>
	<td>1.9</td>
	<td>4.5</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w4a16</td>
	<td>1.87</td>
	<td>5.1</td>
	<td>0.7</td>
	<td>5.2</td>
	<td>0.7</td>
	<td>0.7</td>
	<td>1.3</td>
	<td>3.1</td>
	</tr>
	</table>


	### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
	<table>
	<tr>
	<td></td>
	<td></td>
	<td></td>
	<th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
	</tr>
	<tr>
	<th>GPU class</th>
	<th>Model</th>
	<th>Speedup</th>
	<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
	<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
	<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
	<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
	<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
	<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
	<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
	</tr>
	<tr>
	<td style="vertical-align: middle;" rowspan="3" >A5000</td>
	<td>granite-3.1-2b-base</td>
	<td></td>
	<td>2.9</td>
	<td>10.2</td>
	<td>1.8</td>
	<td>8.2</td>
	<td>19.3</td>
	<td>9.1</td>
	<td>1.3</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
	<td>1.13</td>
	<td>3.1</td>
	<td>12.1</td>
	<td>2.0</td>
	<td>9.6</td>
	<td>22.2</td>
	<td>10.2</td>
	<td>1.4</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w4a16</td>
	<td>0.98</td>
	<td>2.8</td>
	<td>10.0</td>
	<td>1.8</td>
	<td>8.1</td>
	<td>18.6</td>
	<td>9.0</td>
	<td>1.2</td>
	</tr>
	<tr>
	<td style="vertical-align: middle;" rowspan="3" >A6000</td>
	<td>granite-3.1-2b-base</td>
	<td></td>
	<td>3.7</td>
	<td>12.4</td>
	<td>2.4</td>
	<td>10.3</td>
	<td>23.6</td>
	<td>11.0</td>
	<td>1.6</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
	<td>1.12</td>
	<td>3.6</td>
	<td>14.4</td>
	<td>2.7</td>
	<td>12.0</td>
	<td>28.3</td>
	<td>12.9</td>
	<td>1.7</td>
	</tr>
	<tr>
	<td>granite-3.1-2b-base-quantized.w4a16</td>
	<td>0.95</td>
	<td>3.7</td>
	<td>11.4</td>
	<td>2.5</td>
	<td>9.8</td>
	<td>22.1</td>
	<td>10.4</td>
	<td>1.4</td>
	</tr>
	</table>