Instructions to use NilanE/tinyllama-en_ja-translation-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NilanE/tinyllama-en_ja-translation-v3 with Transformers:

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

pipe = pipeline("text-generation", model="NilanE/tinyllama-en_ja-translation-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NilanE/tinyllama-en_ja-translation-v3")
model = AutoModelForCausalLM.from_pretrained("NilanE/tinyllama-en_ja-translation-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NilanE/tinyllama-en_ja-translation-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NilanE/tinyllama-en_ja-translation-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NilanE/tinyllama-en_ja-translation-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NilanE/tinyllama-en_ja-translation-v3

SGLang

How to use NilanE/tinyllama-en_ja-translation-v3 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 "NilanE/tinyllama-en_ja-translation-v3" \
    --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": "NilanE/tinyllama-en_ja-translation-v3",
		"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 "NilanE/tinyllama-en_ja-translation-v3" \
        --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": "NilanE/tinyllama-en_ja-translation-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NilanE/tinyllama-en_ja-translation-v3 with Docker Model Runner:
```
docker model run hf.co/NilanE/tinyllama-en_ja-translation-v3
```

tinyllama-en_ja-translation-v3 / README.md

NilanE

Update README.md

6f8e6ef verified about 2 years ago

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metadata

language:
  - en
  - ja
license: apache-2.0
tags:
  - llama
base_model: NilanE/tinyllama-relora-merge
datasets:
  - NilanE/ParallelFiction-Ja_En-100k

Trained for 2 epochs on NilanE/ParallelFiction-Ja_En-100k using QLoRA. CPO tune is in-progress.

Input should be 500-1000 tokens long. Make sure to set 'do_sample = False' if using HF transformers for inference, or otherwise set temperature to 0 for deterministic outputs.

Prompt format:

Translate this from Japanese to English:
### JAPANESE:
{source_text}
### ENGLISH:

Footnote:

This is an independantly-developed project. If anyone is interested in sponsoring further research please contact nilandekanayake@gmail.com. Questions about model usage can be asked in the disscussion tab.