Instructions to use RedHatAI/Qwen2-72B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen2-72B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Qwen2-72B-Instruct-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Qwen2-72B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen2-72B-Instruct-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/Qwen2-72B-Instruct-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen2-72B-Instruct-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2-72B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen2-72B-Instruct-FP8

SGLang

How to use RedHatAI/Qwen2-72B-Instruct-FP8 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/Qwen2-72B-Instruct-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2-72B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Qwen2-72B-Instruct-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2-72B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen2-72B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen2-72B-Instruct-FP8
```

Update README.md

by abhinavnmagic - opened Jun 11, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+21

-4

Files changed (1) hide show

README.md +21 -4

README.md CHANGED Viewed

@@ -4,11 +4,13 @@ tags:
 - vllm
 ---
-# Qwen2-72B-Instruct-FP8
-Ready to use with `vllm>=0.5.0`.
-Quantized with [AutoFP8](https://github.com/neuralmagic/autofp8) using the following script on 8xA100:
 ```python
 from datasets import load_dataset
@@ -33,4 +35,19 @@ model = AutoFP8ForCausalLM.from_pretrained(
 )
 model.quantize(examples)
 model.save_quantized(quantized_model_dir)
-```

 - vllm
 ---
+# Qwen2-72B-Instruct-FP8
+## Model Overview
+Qwen2-72B-Instruct quantized to FP8 weights and activations using per-tensor quantization, ready for inference with vLLM >= 0.5.0.
+## Usage and Creation
+Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py).
 ```python
 from datasets import load_dataset
 )
 model.quantize(examples)
 model.save_quantized(quantized_model_dir)
+```
+## Evaluation
+### Open LLM Leaderboard evaluation scores
+|                      | Qwen2-72B-Instruct       | Qwen2-72B-Instruct-FP8<br>(this model) |
+| :------------------: | :----------------------: | :------------------------------------------------: |
+| arc-c<br>25-shot     | 71.58                    | 72.09                                              |
+| hellaswag<br>10-shot | 86.94                    | 86.83                                              |
+| mmlu<br>5-shot       | xx.xx                    | 84.06                                              |
+| truthfulqa<br>0-shot | 66.94                    | 66.95                                              |
+| winogrande<br>5-shot | 82.79                    | 83.18                                              |
+| gsm8k<br>5-shot      | xx.xx                    | 88.93                                              |
+| **Average<br>Accuracy**  | **xx.xx**                    |              **80.34**                                     |
+| **Recovery**             | **100%**                     |              **xx.xx%**                                     |