Instructions to use abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1")
model = AutoModelForCausalLM.from_pretrained("abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1", device_map="auto")
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 abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1

SGLang

How to use abeja/ABEJA-Qwen2.5-7b-Japanese-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 "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1" \
    --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": "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1",
		"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 "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1" \
        --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": "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 with Docker Model Runner:
```
docker model run hf.co/abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ABEJA-Qwen2.5-7b-Japanese-v0.1

ABEJA-Qwen2.5-7b-Japanese-v0.1はQwen/Qwen2.5-7B-Instructをベースに日本語の学習をしたモデルです。通常の継続事前学習ではなく、abeja/ABEJA-Qwen2.5-32b-Japanese-v0.1をベースに蒸留学習を実施したモデルです。 Post-Traningは実施しておらず、ChatVector(Qwen/Qwen2.5-7B-InstructとQwen/Qwen2.5-7B の差分ベクトル)により指示追従性能をあげています。

詳細はABEJAのテックブログを参照してください。

使い方

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "人とAIが協調するためには？"
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)