llm-jp-3-150m
llm-jp-3-150m is a 150M-parameter transformer language model developed by Japan's National Institute of Informatics, pre-trained on 2.1T tokens of Japanese, English, code, and multilingual text. It supports both Japanese and English generation with a 4096-token context window. The model is open-source under Apache 2.0, ungated, and suitable for inference and fine-tuning on resource-constrained hardware.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | llm-jp |
| Parameters | 152M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 49.4k |
| Likes | 8 |
| Last updated | 2025-02-04 |
| Source | llm-jp/llm-jp-3-150m |
What llm-jp-3-150m is
Transformer-based causal LM with 12 layers, 512 hidden size, 8 attention heads, and 4096 context length. Pre-trained on mixed-language corpus (Japanese: Wikipedia, Common Crawl, WARP, Kaken; English: Dolma, Wikipedia, Reddit; Code: The Stack; totaling 2.1T tokens). Uses Unigram byte-fallback tokenizer derived from llm-jp-tokenizer v3.0. Distributed in Hugging Face Transformers format with safetensors. Requires torch>=2.3.0, transformers>=4.40.1, and optional flash-attn>=2.5.8 for acceleration.
Run llm-jp-3-150m locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="llm-jp/llm-jp-3-150m")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: bfloat16 precision ~300–600 MB model weights. Inference on single GPU (e.g., RTX 3090, V100, L4) with 8GB+ VRAM is feasible. CPU inference possible but slower. Fine-tuning with LoRA on 16GB consumer GPU practical; full fine-tuning requires 32GB+ or gradient accumulation. Flash-attn optional but recommended for throughput.
150M parameters make LoRA and QLoRA highly feasible on single consumer GPUs (16GB+). No official fine-tuning instructions provided in card. Recommend Hugging Face PEFT library or TRL for instruction tuning. Full fine-tuning (requires 32GB+ VRAM or multi-GPU setup). Tokenizer is fixed; no indication of instruction-tuning or chat-format variants in base model—community or custom adaptation likely required for dialogue use cases.
When to avoid it — and what to weigh
- Requirement for Production-Grade Safety/Alignment — Model card explicitly states 'early stages of research' and 'not tuned to ensure outputs align with human intent and safety considerations.' Unsuitable for customer-facing applications without additional safety guardrails or RLHF fine-tuning.
- Long-Context or Reasoning-Heavy Tasks — 4096-token context window and 150M parameters limit performance on complex reasoning, long document analysis, or multi-hop QA. Larger variants (3.7B, 7.2B) in the LLM-jp-3 family may be needed.
- High-Volume Inference at Scale — No stated benchmarks for throughput, latency, or multi-request serving. Unknown performance at scale compared to distilled models from larger base LLMs. Requires empirical testing before committing to production inference clusters.
- Non-Japanese/English Language Focus — Pre-training corpus is heavily weighted toward Japanese (1T+ tokens) and English. Chinese (0.8B tokens) and Korean (0.3B tokens) support is minimal; expect degraded performance for these languages.
License & commercial use
Apache License 2.0 (OSI-compliant). Permissive open-source license allowing modification, redistribution, and private use. No derivative attribution required in binary form, but license text must be included. Commercial use is permitted under Apache 2.0 terms.
Apache 2.0 is an OSI-approved permissive license that explicitly permits commercial use, modification, and redistribution. No license restrictions on commercial applications. However, model card disclaimers note 'early stages of research' and lack of safety tuning; independent risk assessment and additional safety measures (filtering, monitoring, oversight) are strongly recommended before deploying in production customer-facing systems.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model trained on web-scraped data (Common Crawl, Wikipedia, Reddit, Dolly) and code; inherent risk of memorized sensitive content (emails, credentials, PII). No stated deduplication or PII filtering applied to pre-training corpus. Tokenizer uses byte-fallback; ensure input sanitization if deployed in untrusted environments. No cryptographic integrity checks mentioned. Recommend scanning model card and repo for adversarial examples or prompt-injection vectors if used in chat/interactive contexts.
Alternatives to consider
Mistral-7B
Larger (7B params), English-optimized, more mature community support and serving infrastructure. Better for high-accuracy reasoning; overkill if Japanese is primary requirement and inference latency is critical.
Rinna Japanese GPT-2 (1.2B)
Japanese-specific, comparable size. Trade-off: smaller pre-training, older architecture (GPT-2). Simpler to deploy but lower quality than llm-jp-3-150m Transformer.
LLM-jp-3-440M or 980M
Variants within same family. Larger parameter counts (440M, 980M) offer better quality at cost of higher VRAM/latency. Choose if 150M quality insufficient and deployment budget allows.
Ship llm-jp-3-150m with senior software developers
llm-jp-3-150m offers compact, permissive open-source inference and fine-tuning. Evaluate on your hardware, test with your data, and deploy on-premise or edge infrastructure without licensing constraints. Check the model card for evaluation benchmarks and start with the provided Python snippet.
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llm-jp-3-150m FAQ
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Work with a software development agency
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Ready to Deploy Japanese LLM Locally?
llm-jp-3-150m offers compact, permissive open-source inference and fine-tuning. Evaluate on your hardware, test with your data, and deploy on-premise or edge infrastructure without licensing constraints. Check the model card for evaluation benchmarks and start with the provided Python snippet.