Instructions to use tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3")
model = AutoModelForMultimodalLM.from_pretrained("tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3

SGLang

How to use tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 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 "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3" \
    --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": "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3",
		"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 "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3" \
        --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": "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 with Docker Model Runner:
```
docker model run hf.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3
```

maym15 commited on Apr 2, 2025

Commit

cb201b1

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@@ -286,6 +286,16 @@ Loem and Rio Yokota and Sakae Mizuki},
    month=oct,
    address={University of Pennsylvania, USA},
 }
 ```
 ### References

    month=oct,
    address={University of Pennsylvania, USA},
 }
+@misc{ma:arxiv2025,
+      title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
+      author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
+      year={2025},
+      eprint={2503.23714},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2503.23714},
+}
 ```
 ### References