395 lines
13 KiB
Markdown
395 lines
13 KiB
Markdown
---
|
||
language:
|
||
- en
|
||
- fr
|
||
- de
|
||
- es
|
||
- it
|
||
- pt
|
||
- zh
|
||
- ja
|
||
- ru
|
||
- ko
|
||
base_model:
|
||
- mistralai/Mistral-Small-24B-Instruct-2501
|
||
pipeline_tag: text-generation
|
||
tags:
|
||
- mistral
|
||
- mistral-small
|
||
- quantized
|
||
- W8A8
|
||
- vllm
|
||
- conversational
|
||
- text-generation-inference
|
||
- compressed-tensors
|
||
license: apache-2.0
|
||
license_name: apache-2.0
|
||
name: RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8
|
||
description: This model was obtained by quantizing the weights and activations of Mistral-Small-24B-Instruct-2501 to INT8 data type.
|
||
readme: https://huggingface.co/RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8/main/README.md
|
||
tasks:
|
||
- text-to-text
|
||
provider: Red Hat
|
||
license_link: https://www.apache.org/licenses/LICENSE-2.0
|
||
validated_on:
|
||
- RHOAI 2.20
|
||
- RHAIIS 3.0
|
||
- RHELAI 1.5
|
||
---
|
||
|
||
<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
|
||
Mistral-Small-24B-Instruct-2501-quantized.w8a8
|
||
<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
|
||
</h1>
|
||
|
||
<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
|
||
<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
|
||
</a>
|
||
|
||
## Model Overview
|
||
- **Model Architecture:** Mistral3ForConditionalGeneration
|
||
- **Input:** Text / Image
|
||
- **Output:** Text
|
||
- **Model Optimizations:**
|
||
- **Activation quantization:** INT8
|
||
- **Weight quantization:** INT8
|
||
- **Intended Use Cases:** It is ideal for:
|
||
- Fast-response conversational agents.
|
||
- Low-latency function calling.
|
||
- Subject matter experts via fine-tuning.
|
||
- Local inference for hobbyists and organizations handling sensitive data.
|
||
- Programming and math reasoning.
|
||
- Long document understanding.
|
||
- Visual understanding.
|
||
- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages not officially supported by the model.
|
||
- **Release Date:** 03/03/2025
|
||
- **Version:** 1.0
|
||
- **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
|
||
- **Model Developers:** Red Hat (Neural Magic)
|
||
|
||
|
||
### Model Optimizations
|
||
|
||
This model was obtained by quantizing activations and weights of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501) to INT8 data type.
|
||
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
|
||
Weight quantization also reduces disk size requirements by approximately 50%.
|
||
|
||
Only weights and activations of the linear operators within transformers blocks are quantized.
|
||
Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
|
||
A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
|
||
|
||
|
||
## Deployment
|
||
|
||
1. Initialize vLLM server:
|
||
```
|
||
vllm serve RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8 --tensor_parallel_size 1 --tokenizer_mode mistral
|
||
```
|
||
|
||
2. Send requests to the server:
|
||
|
||
```python
|
||
from openai import OpenAI
|
||
|
||
# Modify OpenAI's API key and API base to use vLLM's API server.
|
||
openai_api_key = "EMPTY"
|
||
openai_api_base = "http://<your-server-host>:8000/v1"
|
||
|
||
client = OpenAI(
|
||
api_key=openai_api_key,
|
||
base_url=openai_api_base,
|
||
)
|
||
|
||
model = "RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8"
|
||
|
||
|
||
messages = [
|
||
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
|
||
]
|
||
|
||
outputs = client.chat.completions.create(
|
||
model=model,
|
||
messages=messages,
|
||
)
|
||
|
||
generated_text = outputs.choices[0].message.content
|
||
print(generated_text)
|
||
```
|
||
|
||
<details>
|
||
<summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
|
||
|
||
```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/Mistral-Small-24B-Instruct-2501-quantized.w8a8
|
||
```
|
||
See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
|
||
</details>
|
||
|
||
<details>
|
||
<summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
|
||
|
||
```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/mistral-small-24b-instruct-2501-quantized-w8a8:1.5
|
||
```
|
||
|
||
```bash
|
||
# Serve model via ilab
|
||
ilab model serve --model-path ~/.cache/instructlab/models/mistral-small-24b-instruct-2501-quantized-w8a8
|
||
|
||
# Chat with model
|
||
ilab model chat --model ~/.cache/instructlab/models/mistral-small-24b-instruct-2501-quantized-w8a8
|
||
```
|
||
See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
|
||
</details>
|
||
|
||
<details>
|
||
<summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
|
||
|
||
```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: mistral-small-24b-instruct-2501-quantized-w8a8 # OPTIONAL CHANGE
|
||
serving.kserve.io/deploymentMode: RawDeployment
|
||
name: mistral-small-24b-instruct-2501-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:
|
||
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-mistral-small-24b-instruct-2501-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": "mistral-small-24b-instruct-2501-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](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
|
||
</details>
|
||
|
||
## Creation
|
||
|
||
<details>
|
||
<summary>Creation details</summary>
|
||
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
|
||
|
||
|
||
```python
|
||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||
from llmcompressor.modifiers.quantization import GPTQModifier
|
||
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
|
||
from llmcompressor.transformers import oneshot
|
||
from datasets import load_dataset
|
||
|
||
# Load model
|
||
model_stub = "mistralai/Mistral-Small-24B-Instruct-2501"
|
||
model_name = model_stub.split("/")[-1]
|
||
|
||
num_samples = 1024
|
||
max_seq_len = 8192
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(model_stub)
|
||
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
model_stub,
|
||
device_map="auto",
|
||
torch_dtype="auto",
|
||
)
|
||
|
||
# Data processing
|
||
def preprocess_text(example):
|
||
text = tokenizer.apply_chat_template(example["messages"], tokenize=False, add_generation_prompt=False)
|
||
return tokenizer(text, padding=False, max_length=max_seq_len, truncation=True)
|
||
|
||
ds = load_dataset("neuralmagic/calibration", name="LLM", split="train").select(range(num_samples))
|
||
ds = ds.map(preprocess_text, remove_columns=ds.column_names)
|
||
|
||
# Configure the quantization algorithm and scheme
|
||
recipe = [
|
||
SmoothQuantModifier(
|
||
smoothing_strength=0.9,
|
||
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"],
|
||
],
|
||
),
|
||
GPTQModifier(
|
||
ignore=["lm_head"],
|
||
sequential_targets=["MistralDecoderLayer"],
|
||
dampening_frac=0.1,
|
||
targets="Linear",
|
||
scheme="W8A8",
|
||
),
|
||
]
|
||
|
||
# Apply quantization
|
||
oneshot(
|
||
model=model,
|
||
dataset=ds,
|
||
recipe=recipe,
|
||
max_seq_length=max_seq_len,
|
||
num_calibration_samples=num_samples
|
||
)
|
||
|
||
# Save to disk in compressed-tensors format
|
||
save_path = model_name + "-quantized.w8a8"
|
||
model.save_pretrained(save_path)
|
||
processor.save_pretrained(save_path)
|
||
print(f"Model and tokenizer saved to: {save_path}")
|
||
```
|
||
</details>
|
||
|
||
|
||
|
||
## Evaluation
|
||
|
||
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
|
||
|
||
OpenLLM Leaderboard V1:
|
||
```
|
||
lm_eval \
|
||
--model vllm \
|
||
--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",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/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
|
||
--apply_chat_template \
|
||
--fewshot_as_multiturn \
|
||
--tasks leaderboard \
|
||
--write_out \
|
||
--batch_size auto \
|
||
--output_path output_dir \
|
||
--show_config
|
||
|
||
```
|
||
|
||
### Accuracy
|
||
|
||
#### OpenLLM Leaderboard V1 evaluation scores
|
||
|
||
| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-quantized.w8a8 |
|
||
|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
|
||
| ARC-Challenge (Acc-Norm, 25-shot) | 72.18 | 68.86 |
|
||
| GSM8K (Strict-Match, 5-shot) | 90.14 | 90.00 |
|
||
| HellaSwag (Acc-Norm, 10-shot) | 85.05 | 85.06 |
|
||
| MMLU (Acc, 5-shot) | 80.69 | 80.25 |
|
||
| TruthfulQA (MC2, 0-shot) | 65.55 | 65.69 |
|
||
| Winogrande (Acc, 5-shot) | 83.11 | 81.69 |
|
||
| **Average Score** | **79.45** | **78.59** |
|
||
| **Recovery (%)** | **100.00** | **98.92** |
|