Instructions to use stockmark/Stockmark-2-100B-Instruct-beta-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stockmark/Stockmark-2-100B-Instruct-beta-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="stockmark/Stockmark-2-100B-Instruct-beta-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta-AWQ")
model = AutoModelForCausalLM.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta-AWQ")
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

vLLM

How to use stockmark/Stockmark-2-100B-Instruct-beta-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stockmark/Stockmark-2-100B-Instruct-beta-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stockmark/Stockmark-2-100B-Instruct-beta-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stockmark/Stockmark-2-100B-Instruct-beta-AWQ

SGLang

How to use stockmark/Stockmark-2-100B-Instruct-beta-AWQ 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 "stockmark/Stockmark-2-100B-Instruct-beta-AWQ" \
    --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": "stockmark/Stockmark-2-100B-Instruct-beta-AWQ",
		"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 "stockmark/Stockmark-2-100B-Instruct-beta-AWQ" \
        --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": "stockmark/Stockmark-2-100B-Instruct-beta-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stockmark/Stockmark-2-100B-Instruct-beta-AWQ with Docker Model Runner:
```
docker model run hf.co/stockmark/Stockmark-2-100B-Instruct-beta-AWQ
```

AWQ quantized model incompatible with bfloat16

by alfredplpl - opened May 19, 2025

Discussion

alfredplpl

May 19, 2025

•

edited May 19, 2025

Hi,

I tried running this model with bfloat16, but encountered an error during inference. It seems that the AWQ quantized version does not support bfloat16 and only works properly with float16.

Could you confirm if this is expected behavior? If so, it might be helpful to document this limitation explicitly, as some users may assume bfloat16 is supported.

Thanks!

omitakahiro

May 20, 2025

Thank you for pointing this out. We've updated the model card to include a usage example and added a note to explicitly mention that float16 should be used when loading the model. Hopefully, this helps avoid confusion for future users.

Thanks!

alfredplpl

May 21, 2025

Thanks.
This point would be helpful for the future users.

alfredplpl changed discussion status to closed May 21, 2025

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