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π New: LFM2.5-VL-450M β our smallest vision model is now available! Learn more β
350M parameter model for bidirectional English-Japanese translation
| Property | Value |
|---|---|
| Parameters | 350M |
| Context Length | 32K tokens |
| Task | Machine Translation |
| Languages | English β Japanese |
"Translate to Japanese." β English β Japanese"Translate to English." β Japanese β Englishpip install "transformers>=5.2.0" torch accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "LiquidAI/LFM2-350M-ENJP-MT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "system", "content": "Translate to Japanese."},
{"role": "user", "content": "What is C. elegans?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
# Output: C. elegansγ¨γ―δ½γ§γγοΌ
messages = [
{"role": "system", "content": "Translate to English."},
{"role": "user", "content": "δ»ζ₯γ―倩ζ°γγγγ§γγγ"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
# Output: The weather is nice today.
hf download LiquidAI/LFM2-350M-ENJP-MT-GGUF \
--local-dir ./LFM2-350M-ENJP-MT-GGUF
# English to Japanese
llama-cli -m ./LFM2-350M-ENJP-MT-GGUF/LFM2-350M-ENJP-MT-Q4_K_M.gguf \
-sys "Translate to Japanese." \
-p "Hello, how are you?"
# Japanese to English
llama-cli -m ./LFM2-350M-ENJP-MT-GGUF/LFM2-350M-ENJP-MT-Q4_K_M.gguf \
-sys "Translate to English." \
-p "γγγ«γ‘γ―γε
ζ°γ§γγοΌ"
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