Instructions to use stockmark/stockmark-13b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stockmark/stockmark-13b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="stockmark/stockmark-13b-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stockmark/stockmark-13b-instruct")
model = AutoModelForCausalLM.from_pretrained("stockmark/stockmark-13b-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use stockmark/stockmark-13b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stockmark/stockmark-13b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stockmark/stockmark-13b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stockmark/stockmark-13b-instruct

SGLang

How to use stockmark/stockmark-13b-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-13b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stockmark/stockmark-13b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-13b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stockmark/stockmark-13b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stockmark/stockmark-13b-instruct with Docker Model Runner:
```
docker model run hf.co/stockmark/stockmark-13b-instruct
```

Stockmark-13b-instruct

Stockmark-13b-instruct is an instruction-tuned version of Stockmark-13b, a 13 billion parameter Japanese LLM. This model is developed by Stockmark Inc.

We used data (2023/11/03 version) from Project of Development of Japanese Instruction data for LLM for instruction tuning.

Please see our blog for more details.

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("stockmark/stockmark-13b-instruct", device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("stockmark/stockmark-13b-instruct")

instruction = "自然言語処理とは？"

prompt = f"""### Input:
{instruction}

### Output:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    tokens = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=True,
        temperature=0.7
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)

Training dataset

Project of Development of Japanese Instruction data for LLM

License

MIT

Developed by

Stockmark Inc.

Author

Takahiro Omi

Downloads last month: 14

Safetensors

Model size

13B params

Tensor type

BF16