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

Libraries

How to use stockmark/Stockmark-2-100B-Instruct-beta 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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta")
model = AutoModelForCausalLM.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

omitakahiro commited on Mar 5, 2025

Commit

dca4e8a

verified ·

1 Parent(s): 6330e4a

Update README.md

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README.md +8 -4

README.md CHANGED Viewed

@@ -27,15 +27,19 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta")
-model = AutoModelForCausalLM.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta", device_map="auto", torch_dtype=torch.bfloat16)
-instruction = ""
-input_ids = tokenizer(instruction, add_generation_prompt=True).input_ids.to(model.device)
 with torch.inference_mode():
     tokens = model.generate(
         input_ids,
-        max_new_tokens = 1024,
         do_sample = True,
         temperature = 0.7,
         top_p = 0.95,

 from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta")
+model = AutoModelForCausalLM.from_pretrained(
+    "stockmark/Stockmark-2-100B-Instruct-beta", device_map="auto", torch_dtype=torch.bfloat16
+)
+instruction = "自然言語処理とは？"
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": instruction}], add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
 with torch.inference_mode():
     tokens = model.generate(
         input_ids,
+        max_new_tokens = 256,
         do_sample = True,
         temperature = 0.7,
         top_p = 0.95,