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)

# 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")
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 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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Stockmark-2-100B-Instruct-beta

Model description

Stockmark-2-100B-Instruct-beta is a 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. It was pre-trained on approximately 1.5 trillion tokens of data, consisting of 60% English, 30% Japanese, and 10% code. Following pretraining, the model underwent post-training with synthetic data in Japanese to enhance its ability to follow instructions. This synthetic data was generated using Qwen2.5-32B-Instruct.

As a beta release, Stockmark-2-100b-Instruct-beta is still undergoing improvements and evaluations. Feedback and insights from users will help refine future versions.

See our blog for the detail.

This project is supported by GENIAC.

How to use

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 = "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 = 512,
        do_sample = True,
        temperature = 0.7,
        top_p = 0.95,
        repetition_penalty = 1.05
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)