gemma-2-9b-it-FP8

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Model Overview

• Model Architecture: Gemma 2
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Intended Use Cases: Intended for commercial and research use in English. Similarly to gemma-2-9b-it, 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: 7/8/2024
• Version: 1.0
• Validated on: RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
• License(s): gemma
• Model Developers: Neural Magic (Red Hat)

Quantized version of gemma-2-9b-it. It achieves an average score of 73.49 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 73.23.

Model Optimizations

This model was obtained by quantizing the weights and activations of gemma-2-9b-it to FP8 data type, ready for inference with vLLM >= 0.5.1. 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. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. AutoFP8 is used for quantization with a single instance of every token in random order.

Deployment

Use with vLLM

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

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/gemma-2-9b-it-FP8"

sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

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

prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

llm = LLM(model=model_id)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

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

<details> <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>

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/gemma-2-9b-it-FP8

​​See Red Hat AI Inference Server documentation for more details.

</details> <details> <summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>

# 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/gemma-2-9b-it-FP8:1.5

# Serve model via ilab
ilab model serve --model-path ~/.cache/instructlab/models/gemma-2-9b-it-FP8
  
# Chat with model
ilab model chat --model ~/.cache/instructlab/models/gemma-2-9b-it-FP8

See Red Hat Enterprise Linux AI documentation for more details.

</details> <details> <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>

# 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

# 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: gemma-2-9b-it-FP8 # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: gemma-2-9b-it-FP8         # 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:
      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-gemma-2-9b-it-FP8:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists

# 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

# 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": "gemma-2-9b-it-FP8",
    "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.

</details>

Creation

This model was created by applying AutoFP8 with calibration samples from ultrachat, as presented in the code snipet below. Although AutoFP8 was used for this particular model, Neural Magic is transitioning to using llm-compressor which supports several quantization schemes and models not supported by AutoFP8.

from datasets import load_dataset
from transformers import AutoTokenizer
import numpy as np
import torch

from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

MODEL_DIR = "google/gemma-2-9b-it"
final_model_dir = MODEL_DIR.split("/")[-1]

CONTEXT_LENGTH = 4096
NUM_SAMPLES = 512
NUM_REPEATS = 1

pretrained_model_dir = MODEL_DIR
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=CONTEXT_LENGTH)
tokenizer.pad_token = tokenizer.eos_token

tokenizer_num_tokens = len(list(tokenizer.get_vocab().values()))
total_token_samples = NUM_REPEATS * tokenizer_num_tokens
num_random_samp = -(-total_token_samples // CONTEXT_LENGTH)

input_ids = np.tile(np.arange(tokenizer_num_tokens), NUM_REPEATS + 1)[:num_random_samp * CONTEXT_LENGTH]
np.random.shuffle(input_ids)
input_ids = input_ids.reshape(num_random_samp, CONTEXT_LENGTH)
input_ids = torch.tensor(input_ids, dtype=torch.int64).to("cuda")

quantize_config = BaseQuantizeConfig(
    quant_method="fp8",
    activation_scheme="static",
)

examples = input_ids

model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config=quantize_config)

model.quantize(examples)

quantized_model_dir = f"{final_model_dir}-FP8"
model.save_quantized(quantized_model_dir)

Evaluation

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

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/gemma-2-9b-it-FP8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
  --tasks openllm \
  --batch_size auto

Accuracy

Open LLM Leaderboard evaluation scores

<table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>gemma-2-9b-it</strong> </td> <td><strong>gemma-2-9b-it-FP8(this model)</strong> </td> <td><strong>Recovery</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>72.28 </td> <td>71.99 </td> <td>99.59% </td> </tr> <tr> <td>ARC Challenge (25-shot) </td> <td>71.50 </td> <td>71.50 </td> <td>100.0% </td> </tr> <tr> <td>GSM-8K (5-shot, strict-match) </td> <td>76.26 </td> <td>76.87 </td> <td>100.7% </td> </tr> <tr> <td>Hellaswag (10-shot) </td> <td>81.91 </td> <td>81.70 </td> <td>99.74% </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>77.11 </td> <td>78.37 </td> <td>101.6% </td> </tr> <tr> <td>TruthfulQA (0-shot) </td> <td>60.32 </td> <td>60.52 </td> <td>100.3% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>73.23</strong> </td> <td><strong>73.49</strong> </td> <td><strong>100.36%</strong> </td> </tr> </table>