Instructions to use mpasila/JP-EN-Translator-2K-steps-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/JP-EN-Translator-2K-steps-7B with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/JP-EN-Translator-2K-steps-7B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/JP-EN-Translator-2K-steps-7B")
model = AutoModelForCausalLM.from_pretrained("mpasila/JP-EN-Translator-2K-steps-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mpasila/JP-EN-Translator-2K-steps-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mpasila/JP-EN-Translator-2K-steps-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mpasila/JP-EN-Translator-2K-steps-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mpasila/JP-EN-Translator-2K-steps-7B

SGLang

How to use mpasila/JP-EN-Translator-2K-steps-7B 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 "mpasila/JP-EN-Translator-2K-steps-7B" \
    --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": "mpasila/JP-EN-Translator-2K-steps-7B",
		"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 "mpasila/JP-EN-Translator-2K-steps-7B" \
        --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": "mpasila/JP-EN-Translator-2K-steps-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use mpasila/JP-EN-Translator-2K-steps-7B with Docker Model Runner:
```
docker model run hf.co/mpasila/JP-EN-Translator-2K-steps-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Experimental model, may not perform that well. Dataset used is a modified version of NilanE/ParallelFiction-Ja_En-100k.

After training with an 8k context length it didn't appear to improve performance much at all. Not sure if I should keep training it (which is costly) or if I should fix some issues with the dataset (like it starting with Ch or Chapter) or I go back to finetuning Finnish models.

Prompt format: Alpaca

Below is a translation task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}

Uploaded model

Developed by: mpasila
License: apache-2.0
Finetuned from model : augmxnt/shisa-base-7b-v1

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for mpasila/JP-EN-Translator-2K-steps-7B

Base model

augmxnt/shisa-base-7b-v1

Finetuned

(4)

this model

Quantizations

2 models

mpasila
/

JP-EN-Translator-2K-steps-7B

Prompt format: Alpaca

Uploaded model

Model tree for mpasila/JP-EN-Translator-2K-steps-7B

Datasets used to train mpasila/JP-EN-Translator-2K-steps-7B