Instructions to use Kendamarron/Tokara-0.5B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kendamarron/Tokara-0.5B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Kendamarron/Tokara-0.5B-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kendamarron/Tokara-0.5B-v0.1")
model = AutoModelForCausalLM.from_pretrained("Kendamarron/Tokara-0.5B-v0.1")
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

vLLM

How to use Kendamarron/Tokara-0.5B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kendamarron/Tokara-0.5B-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": "Kendamarron/Tokara-0.5B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kendamarron/Tokara-0.5B-v0.1

SGLang

How to use Kendamarron/Tokara-0.5B-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 "Kendamarron/Tokara-0.5B-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": "Kendamarron/Tokara-0.5B-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 "Kendamarron/Tokara-0.5B-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": "Kendamarron/Tokara-0.5B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kendamarron/Tokara-0.5B-v0.1 with Docker Model Runner:
```
docker model run hf.co/Kendamarron/Tokara-0.5B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

モデルについて

Qwen/Qwen1.5-0.5Bを日英データ5Bトークンで継続事前学習したモデルです。

ベンチマークのスコアは低下していますが、ベースモデルよりも安定して日本語を出力するようになっています。

詳細はこちらをご覧ください。

ベンチマーク

Stability-AI/lm-evaluation-harnessの3項目で評価

モデル	jsquad(1-shot)	jcommonsenseqa(1-shot)	jnli(1-shot)
Kendamarron/Tokara-0.5B-v0.1	26.4295	0.2663	0.5509
Qwen/Qwen1.5-0.5B	31.3597	0.2556	0.5534

名前について

日本の在来馬であるトカラ馬から

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = AutoModelForCausalLM.from_pretrained('Kendamarron/Tokara-0.5B-v0.1')
tokenizer = AutoTokenizer.from_pretrained('Kendamarron/Tokara-0.5B-v0.1')

pipe = pipeline('text-generation', model=model, tokenizer=tokenizer)

prompt = "大規模言語モデルとは、"

print(pipe(prompt, max_length=128, repetition_penalty=1.1, temperature=0.7, top_p=0.95))

Downloads last month: 3

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for Kendamarron/Tokara-0.5B-v0.1

Finetunes

2 models

Quantizations

1 model

Datasets used to train Kendamarron/Tokara-0.5B-v0.1

Collection including Kendamarron/Tokara-0.5B-v0.1

Tokara-0.5B

Collection

3 items • Updated May 8, 2024 • 1