Instructions to use tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1")

Inference
Local Apps Settings

vLLM

How to use tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1

SGLang

How to use tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 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/Gemma-2-Llama-Swallow-27b-pt-v0.1" \
    --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": "tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1",
		"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 "tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1" \
        --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": "tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 with Docker Model Runner:
```
docker model run hf.co/tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1
```

Taishi-N324 commited on May 18, 2025

Commit

06d9346

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Update README.md

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@@ -9,9 +9,9 @@ license:
   - llama3.3
 ---
-# Gemma-2-Llama Swallow
-Gemma-2-Llama Swallow series was built by continual pre-training on the [gemma-2](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315) models.
 Gemma 2 Swallow enhanced the Japanese language capabilities of the original Gemma 2 while retaining the English language capabilities.
 We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
 The instruction-tuned models (it) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
@@ -50,49 +50,49 @@ The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index
 ### Japanese tasks
-| Model                                           | JCom.  | JEMHopQA | NIILC   | JSQuAD  | XL-Sum  | MGSM   | WMT20-en-ja | WMT20-ja-en | JMMLU  | JHumanEval | Ja Avg |
-| ----------------------------------------------- | ------ | -------- | ------- | ------- | ------- | ------ | ----------- | ----------- | ------ | ---------- | ------ |
-|                                                 | 4-shot | 4-shot   | 4-shot  | 4-shot  | 1-shot  | 4-shot | 4-shot      | 4-shot      | 5-shot | 0-shot     |        |
-|                                                 | EM acc | Char-F1  | Char-F1 | Char-F1 | ROUGE-2 | EM acc | BLEU        | BLEU        | EM acc | pass@1     |        |
-| google/gemma-3-1b-pt                            | 0.237  | 0.410    | 0.252   | 0.631   | 0.079   | 0.024  | 0.150       | 0.136       | 0.239  | 0.073      | 0.223  |
-| Qwen/Qwen2.5-1.5B                               | 0.800  | 0.383    | 0.241   | 0.849   | 0.143   | 0.292  | 0.132       | 0.134       | 0.438  | 0.308      | 0.372  |
-| google/gemma-2-2b                               | 0.721  | 0.472    | 0.316   | 0.810   | 0.083   | 0.124  | 0.203       | 0.190       | 0.388  | 0.177      | 0.348  |
-| rinna/gemma-2-baku-2b                           | 0.760  | 0.475    | 0.443   | 0.843   | 0.121   | 0.124  | 0.255       | 0.187       | 0.376  | 0.137      | 0.372  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-2b-pt-v0.1  | 0.830  | 0.509    | 0.549   | 0.863   | 0.119   | 0.172  | 0.261       | 0.195       | 0.461  | 0.251      | 0.421  |
-| Qwen/Qwen2.5-3B                                 | 0.847  | 0.475    | 0.306   | 0.878   | 0.176   | 0.460  | 0.180       | 0.167       | 0.529  | 0.404      | 0.442  |
-| google/gemma-3-4b-pt                            | 0.851  | 0.432    | 0.410   | 0.887   | 0.139   | 0.248  | 0.230       | 0.205       | 0.499  | 0.273      | 0.417  |
-| Qwen/Qwen2.5-7B                                 | 0.924  | 0.459    | 0.426   | 0.907   | 0.216   | 0.616  | 0.229       | 0.199       | 0.634  | 0.507      | 0.512  |
-| tokyotech-llm/Llama-3.1-Swallow-8B-v0.2         | 0.911  | 0.510    | 0.627   | 0.892   | 0.198   | 0.464  | 0.296       | 0.233       | 0.525  | 0.336      | 0.499  |
-| google/gemma-2-9b                               | 0.904  | 0.573    | 0.524   | 0.898   | 0.168   | 0.456  | 0.269       | 0.236       | 0.623  | 0.345      | 0.500  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1  | 0.950  | 0.643    | 0.677   | 0.897   | 0.187   | 0.560  | 0.304       | 0.247       | 0.650  | 0.462      | 0.558  |
-| google/gemma-3-12b-pt                           | 0.787  | 0.563    | 0.569   | 0.911   | 0.194   | 0.584  | 0.288       | 0.244       | 0.659  | 0.385      | 0.518  |
-| google/gemma-2-27b                              | 0.936  | 0.553    | 0.573   | 0.916   | 0.194   | 0.596  | 0.295       | 0.251       | 0.659  | 0.490      | 0.546  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 | 0.958  | 0.660    | 0.671   | 0.924   | 0.200   | 0.644  | 0.321       | 0.255       | 0.679  | 0.629      | 0.594  |
-| google/gemma-3-27b-pt                           | 0.944  | 0.582    | 0.627   | 0.915   | 0.210   | 0.704  | 0.301       | 0.255       | 0.724  | 0.473      | 0.574  |
-| Qwen/Qwen2.5-32B                                | 0.961  | 0.561    | 0.538   | 0.925   | 0.228   | 0.808  | 0.271       | 0.233       | 0.751  | 0.637      | 0.591  |
 ### English tasks
-| Model                                           | OpenBookQA | TriviaQA | HellaSWAG | SQuAD2.0 | XWINO  | MMLU   | GSM8K  | MATH       | BBH        | HumanEval | En Avg |
-| ----------------------------------------------- | ---------- | -------- | --------- | -------- | ------ | ------ | ------ | ---------- | ---------- | --------- | ------ |
-|                                                 | 4-shot     | 4-shot   | 4-shot    | 4-shot   | 4-shot | 5-shot | 4-shot | 4-shot     | 3-shot     | 0-shot    |        |
-|                                                 | Acc        | EM acc   | Acc       | EM acc   | Acc    | Acc    | EM acc | CoT EM Acc | CoT EM Acc | pass@1    |        |
-| google/gemma-3-1b-pt                            | 0.304      | 0.358    | 0.471     | 0.501    | 0.832  | 0.262  | 0.016  | 0.008      | 0.276      | 0.070     | 0.310  |
-| Qwen/Qwen2.5-1.5B                               | 0.342      | 0.397    | 0.499     | 0.506    | 0.851  | 0.610  | 0.611  | 0.314      | 0.413      | 0.356     | 0.490  |
-| google/gemma-2-2b                               | 0.342      | 0.552    | 0.552     | 0.501    | 0.890  | 0.530  | 0.249  | 0.176      | 0.415      | 0.188     | 0.439  |
-| rinna/gemma-2-baku-2b                           | 0.314      | 0.475    | 0.533     | 0.501    | 0.881  | 0.493  | 0.168  | 0.110      | 0.376      | 0.150     | 0.400  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-2b-pt-v0.1  | 0.312      | 0.435    | 0.516     | 0.501    | 0.871  | 0.538  | 0.275  | 0.144      | 0.384      | 0.286     | 0.426  |
-| Qwen/Qwen2.5-3B                                 | 0.360      | 0.504    | 0.553     | 0.541    | 0.872  | 0.657  | 0.580  | 0.440      | 0.442      | 0.387     | 0.534  |
-| google/gemma-3-4b-pt                            | 0.360      | 0.603    | 0.576     | 0.502    | 0.895  | 0.596  | 0.376  | 0.258      | 0.495      | 0.351     | 0.501  |
-| Qwen/Qwen2.5-7B                                 | 0.392      | 0.601    | 0.600     | 0.618    | 0.888  | 0.742  | 0.832  | 0.510      | 0.562      | 0.554     | 0.630  |
-| tokyotech-llm/Llama-3.1-Swallow-8B-v0.2         | 0.382      | 0.651    | 0.596     | 0.513    | 0.904  | 0.622  | 0.521  | 0.228      | 0.605      | 0.366     | 0.539  |
-| google/gemma-2-9b                               | 0.382      | 0.718    | 0.626     | 0.506    | 0.907  | 0.706  | 0.688  | 0.338      | 0.704      | 0.390     | 0.597  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1  | 0.362      | 0.659    | 0.602     | 0.532    | 0.906  | 0.687  | 0.678  | 0.330      | 0.664      | 0.529     | 0.595  |
-| google/gemma-3-12b-pt                           | 0.398      | 0.747    | 0.637     | 0.524    | 0.917  | 0.737  | 0.703  | 0.398      | 0.683      | 0.445     | 0.619  |
-| google/gemma-2-27b                              | 0.412      | 0.780    | 0.675     | 0.549    | 0.921  | 0.754  | 0.757  | 0.438      | 0.760      | 0.508     | 0.655  |
-| tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1 | 0.414      | 0.756    | 0.652     | 0.597    | 0.915  | 0.749  | 0.732  | 0.416      | 0.765      | 0.658     | 0.665  |
-| google/gemma-3-27b-pt                           | 0.414      | 0.809    | 0.667     | 0.618    | 0.923  | 0.780  | 0.801  | 0.520      | 0.732      | 0.507     | 0.677  |
-| Qwen/Qwen2.5-32B                                | 0.406      | 0.664    | 0.656     | 0.668    | 0.913  | 0.832  | 0.718  | 0.600      | 0.717      | 0.523     | 0.670  |
 ## Evaluation Benchmarks

   - llama3.3
 ---
+# Gemma-2-Llama-Swallow
+Gemma-2-Llama-Swallow series was built by continual pre-training on the [gemma-2](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315) models.
 Gemma 2 Swallow enhanced the Japanese language capabilities of the original Gemma 2 while retaining the English language capabilities.
 We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
 The instruction-tuned models (it) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
 ### Japanese tasks
+| Model                                               | JCom.  | JEMHopQA | NIILC   | JSQuAD  | XL-Sum  | MGSM   | WMT20-en-ja | WMT20-ja-en | JMMLU  | JHumanEval | Ja Avg |
+| --------------------------------------------------- | ------ | -------- | ------- | ------- | ------- | ------ | ----------- | ----------- | ------ | ---------- | ------ |
+|                                                     | 4-shot | 4-shot   | 4-shot  | 4-shot  | 1-shot  | 4-shot | 4-shot      | 4-shot      | 5-shot | 0-shot     |        |
+|                                                     | EM acc | Char-F1  | Char-F1 | Char-F1 | ROUGE-2 | EM acc | BLEU        | BLEU        | EM acc | pass@1     |        |
+| google/gemma-3-1b-pt                                | 0.237  | 0.410    | 0.252   | 0.631   | 0.079   | 0.024  | 0.150       | 0.136       | 0.239  | 0.073      | 0.223  |
+| Qwen/Qwen2.5-1.5B                                   | 0.800  | 0.383    | 0.241   | 0.849   | 0.143   | 0.292  | 0.132       | 0.134       | 0.438  | 0.308      | 0.372  |
+| google/gemma-2-2b                                   | 0.721  | 0.472    | 0.316   | 0.810   | 0.083   | 0.124  | 0.203       | 0.190       | 0.388  | 0.177      | 0.348  |
+| rinna/gemma-2-baku-2b                               | 0.760  | 0.475    | 0.443   | 0.843   | 0.121   | 0.124  | 0.255       | 0.187       | 0.376  | 0.137      | 0.372  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-2b-pt-v0.1**  | 0.830  | 0.509    | 0.549   | 0.863   | 0.119   | 0.172  | 0.261       | 0.195       | 0.461  | 0.251      | 0.421  |
+| Qwen/Qwen2.5-3B                                     | 0.847  | 0.475    | 0.306   | 0.878   | 0.176   | 0.460  | 0.180       | 0.167       | 0.529  | 0.404      | 0.442  |
+| google/gemma-3-4b-pt                                | 0.851  | 0.432    | 0.410   | 0.887   | 0.139   | 0.248  | 0.230       | 0.205       | 0.499  | 0.273      | 0.417  |
+| Qwen/Qwen2.5-7B                                     | 0.924  | 0.459    | 0.426   | 0.907   | 0.216   | 0.616  | 0.229       | 0.199       | 0.634  | 0.507      | 0.512  |
+| tokyotech-llm/Llama-3.1-Swallow-8B-v0.2             | 0.911  | 0.510    | 0.627   | 0.892   | 0.198   | 0.464  | 0.296       | 0.233       | 0.525  | 0.336      | 0.499  |
+| google/gemma-2-9b                                   | 0.904  | 0.573    | 0.524   | 0.898   | 0.168   | 0.456  | 0.269       | 0.236       | 0.623  | 0.345      | 0.500  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1**  | 0.950  | 0.643    | 0.677   | 0.897   | 0.187   | 0.560  | 0.304       | 0.247       | 0.650  | 0.462      | 0.558  |
+| google/gemma-3-12b-pt                               | 0.787  | 0.563    | 0.569   | 0.911   | 0.194   | 0.584  | 0.288       | 0.244       | 0.659  | 0.385      | 0.518  |
+| google/gemma-2-27b                                  | 0.936  | 0.553    | 0.573   | 0.916   | 0.194   | 0.596  | 0.295       | 0.251       | 0.659  | 0.490      | 0.546  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1** | 0.958  | 0.660    | 0.671   | 0.924   | 0.200   | 0.644  | 0.321       | 0.255       | 0.679  | 0.629      | 0.594  |
+| google/gemma-3-27b-pt                               | 0.944  | 0.582    | 0.627   | 0.915   | 0.210   | 0.704  | 0.301       | 0.255       | 0.724  | 0.473      | 0.574  |
+| Qwen/Qwen2.5-32B                                    | 0.961  | 0.561    | 0.538   | 0.925   | 0.228   | 0.808  | 0.271       | 0.233       | 0.751  | 0.637      | 0.591  |
 ### English tasks
+| Model                                               | OpenBookQA | TriviaQA | HellaSWAG | SQuAD2.0 | XWINO  | MMLU   | GSM8K  | MATH       | BBH        | HumanEval | En Avg |
+| --------------------------------------------------- | ---------- | -------- | --------- | -------- | ------ | ------ | ------ | ---------- | ---------- | --------- | ------ |
+|                                                     | 4-shot     | 4-shot   | 4-shot    | 4-shot   | 4-shot | 5-shot | 4-shot | 4-shot     | 3-shot     | 0-shot    |        |
+|                                                     | Acc        | EM acc   | Acc       | EM acc   | Acc    | Acc    | EM acc | CoT EM Acc | CoT EM Acc | pass@1    |        |
+| google/gemma-3-1b-pt                                | 0.304      | 0.358    | 0.471     | 0.501    | 0.832  | 0.262  | 0.016  | 0.008      | 0.276      | 0.070     | 0.310  |
+| Qwen/Qwen2.5-1.5B                                   | 0.342      | 0.397    | 0.499     | 0.506    | 0.851  | 0.610  | 0.611  | 0.314      | 0.413      | 0.356     | 0.490  |
+| google/gemma-2-2b                                   | 0.342      | 0.552    | 0.552     | 0.501    | 0.890  | 0.530  | 0.249  | 0.176      | 0.415      | 0.188     | 0.439  |
+| rinna/gemma-2-baku-2b                               | 0.314      | 0.475    | 0.533     | 0.501    | 0.881  | 0.493  | 0.168  | 0.110      | 0.376      | 0.150     | 0.400  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-2b-pt-v0.1**  | 0.312      | 0.435    | 0.516     | 0.501    | 0.871  | 0.538  | 0.275  | 0.144      | 0.384      | 0.286     | 0.426  |
+| Qwen/Qwen2.5-3B                                     | 0.360      | 0.504    | 0.553     | 0.541    | 0.872  | 0.657  | 0.580  | 0.440      | 0.442      | 0.387     | 0.534  |
+| google/gemma-3-4b-pt                                | 0.360      | 0.603    | 0.576     | 0.502    | 0.895  | 0.596  | 0.376  | 0.258      | 0.495      | 0.351     | 0.501  |
+| Qwen/Qwen2.5-7B                                     | 0.392      | 0.601    | 0.600     | 0.618    | 0.888  | 0.742  | 0.832  | 0.510      | 0.562      | 0.554     | 0.630  |
+| tokyotech-llm/Llama-3.1-Swallow-8B-v0.2             | 0.382      | 0.651    | 0.596     | 0.513    | 0.904  | 0.622  | 0.521  | 0.228      | 0.605      | 0.366     | 0.539  |
+| google/gemma-2-9b                                   | 0.382      | 0.718    | 0.626     | 0.506    | 0.907  | 0.706  | 0.688  | 0.338      | 0.704      | 0.390     | 0.597  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-9b-pt-v0.1**  | 0.362      | 0.659    | 0.602     | 0.532    | 0.906  | 0.687  | 0.678  | 0.330      | 0.664      | 0.529     | 0.595  |
+| google/gemma-3-12b-pt                               | 0.398      | 0.747    | 0.637     | 0.524    | 0.917  | 0.737  | 0.703  | 0.398      | 0.683      | 0.445     | 0.619  |
+| google/gemma-2-27b                                  | 0.412      | 0.780    | 0.675     | 0.549    | 0.921  | 0.754  | 0.757  | 0.438      | 0.760      | 0.508     | 0.655  |
+| **tokyotech-llm/Gemma-2-Llama-Swallow-27b-pt-v0.1** | 0.414      | 0.756    | 0.652     | 0.597    | 0.915  | 0.749  | 0.732  | 0.416      | 0.765      | 0.658     | 0.665  |
+| google/gemma-3-27b-pt                               | 0.414      | 0.809    | 0.667     | 0.618    | 0.923  | 0.780  | 0.801  | 0.520      | 0.732      | 0.507     | 0.677  |
+| Qwen/Qwen2.5-32B                                    | 0.406      | 0.664    | 0.656     | 0.668    | 0.913  | 0.832  | 0.718  | 0.600      | 0.717      | 0.523     | 0.670  |
 ## Evaluation Benchmarks