README.md

---
datasets:
- lmg-anon/VNTL-v3.1-1k
language:
- en
- ja
base_model:
- Qwen/Qwen2.5-7B
pipeline_tag: translation
tags:
- translation
- vntl
- qwen
license: apache-2.0
library_name: transformers
---
# Qwen2.5-7B-VNTL-JP-EN

Qwen2.5-7B finetuned for Japanese to English translation.

Trained on ~150k sentences from [VNTL-v3.1-1k](https://huggingface.co/datasets/lmg-anon/VNTL-v3.1-1k).

The model was trained on just the sentences in random order to make it more flexible and useful outside of just VN translation.

## Usage

### Ollama

1. `ollama run technobyte/Qwen2.5-7B-VNTL-JP-EN:q4_k_m`
2. Input just the Japanese sentence.

### Llama.cpp

1. Download the [GGUF](https://huggingface.co/TechnoByte/Qwen2.5-7B-VNTL-JP-EN-GGUF/tree/main).
2. `llama-cli -m Qwen2.5-7B-VNTL-JP-EN-Q4_K_M.gguf -no-cnv -p "A Japanese sentence along with a proper English equivalent.\nJapanese: 放課後はマンガ喫茶でまったり〜♡ おすすめのマンガ教えて！ \nEnglish: "`

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TechnoByte/Qwen2.5-7B-VNTL-JP-EN"

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

messages = [
    {"role": "user", "content": "放課後はマンガ喫茶でまったり〜♡ おすすめのマンガ教えて！"}
]
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]
```

## Prompt template

### Plaintext

```
A Japanese sentence along with a proper English equivalent.
Japanese: JAPANESE SENTENCE HERE
English: 
```

### Jinja (HF Transformers)

```jinja
{% for i in range(0, messages|length, 2) %}A Japanese sentence along with a proper English equivalent.
Japanese: {{ messages[i].content }}
English:{% if i+1 < messages|length %} {{ messages[i+1].content }}<|endoftext|>{{ "
" }}{% else %}{% endif %}{% endfor %}
```

### Go (Ollama)

```
A Japanese sentence along with a proper English equivalent.
Japanese: {{ .Prompt }}
English: {{ .Response }}<|endoftext|>
```

## Limitations

- Can only translate one sentence per turn.
- Can use incorrect pronouns due to lack of context.