nagohachi/tiny-lm-japanese-500m-sft-v1
050
tiny-lm-japanese-500m-sft-v1
Instruction-tuned (SFT) version of tiny-lm-japanese-500m-base-v1, a ~500M-parameter Japanese LLM pretrained from scratch on ~10B tokens. See tiny-lm-japanese-500m-dpo-v1 for the preference-aligned variant.
The model uses a custom Llama-style implementation shipped with the repo (trust_remote_code=True required).
Model details
Training
- Data: llm-jp/magpie-sft-v1.0
- kanhatakeyama/AutoMultiTurnByCalm3-22B (~188k conversations, loss on assistant spans only)
- Schedule: 277M tokens
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "nagohachi/tiny-lm-japanese-500m-sft-v1"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, dtype="bfloat16")
messages = [{"role": "user", "content": "日本の首都はどこですか？"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.9)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))Evaluation
llm-jp-eval (v2.1.5)
Base models use the 4-shot setting; SFT/DPO models and the instruct baseline are prompted through their chat template.