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

Libraries

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

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

pipe = pipeline("text-generation", model="stockmark/Stockmark-2-100B-Instruct")
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")
model = AutoModelForCausalLM.from_pretrained("stockmark/Stockmark-2-100B-Instruct")
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 stockmark/Stockmark-2-100B-Instruct 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use stockmark/Stockmark-2-100B-Instruct with Docker Model Runner:
```
docker model run hf.co/stockmark/Stockmark-2-100B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Stockmark-2-100B-Instruct

Model description

Stockmark-2-100B-Instruct is a 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. It was pre-trained on approximately 2.0 trillion tokens of data, consisting of 60% English, 30% Japanese, and 10% code. Following pretraining, the model underwent post-training (SFT and DPO) with synthetic data in Japanese to enhance its ability to follow instructions. This version improves instruction-following ability and adds support for long-context (32k), compared to the previous version (Stockmark-2-100B-Instruct-beta).

This project was supported by GENIAC.

Features

Model Type: Causal Language Model
Number of Parameters: 96B
Number of Layers: 86
Number of Attention Heads (GQA): 72 for Q and 8 for KV
Context Length: 32k
Supported Languages: Japanese and English

Model performance

Japanese MT-bench

Model	Average	coding	extraction	humanities	math	reasoning	roleplay	stem
Stockmark-2-100B-Instruct	7.87	7.07	8.35	8.73	7.57	5.45	8.65	8.33
Stockmark-2-100B-Instruct-beta	7.71	6.73	8.23	8.63	7.01	5.85	8.54	8.07

How to use

transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "stockmark/Stockmark-2-100B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="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
  )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)

vLLM

from vllm import LLM, SamplingParams

llm = LLM(
    model="stockmark/Stockmark-2-100B-Instruct",
    tensor_parallel_size=4,
    dtype="bfloat16"
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.95,
    max_tokens=512
)

conversation = [{"role": "user", "content": "自然言語処理とは？"}]

outputs = llm.chat(conversation, sampling_params=sampling_params)

for output in outputs:
    generated_text = output.outputs[0].text
    print(generated_text)