japanese-gpt-neox-small
japanese-gpt-neox-small is a lightweight, open-source Japanese language model with 203M parameters trained on Japanese CC-100, Wikipedia, and MC4 datasets. It uses a 12-layer transformer architecture and supports standard PyTorch/Hugging Face inference. The MIT license permits commercial use. The model is not gated and has modest community engagement (15 likes, 292k downloads).
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | rinna |
| Parameters | 204M |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 292.6k |
| Likes | 15 |
| Last updated | 2025-03-23 |
| Source | rinna/japanese-gpt-neox-small |
What japanese-gpt-neox-small is
A GPT-NeoX-based causal language model (12 layers, 768 hidden units) optimized for Japanese text generation. Trained with a standard language modeling objective on multilingual and Japanese corpora. Uses SentencePiece tokenization. Verified compatible with NVIDIA FasterTransformer 5.1+ for optimized inference. Last updated March 2025. Released September 2022.
Run japanese-gpt-neox-small locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="rinna/japanese-gpt-neox-small")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: ~800 MB–1.2 GB VRAM (fp32); ~400–600 MB (fp16); ~200–300 MB (int8/int4 quantized). CPU inference possible but slow. GPU (e.g., NVIDIA T4 or better) recommended for production. FasterTransformer can further reduce memory and latency. Verify on your exact deployment target.
Card explicitly documents prefix-tuning support with working example code. LoRA/QLoRA feasibility is plausible given standard transformer architecture but not stated; test empirically. No guidance on instruction-tuning or chat alignment provided. Start with prefix-tuning if adapting for specific tasks; LoRA is likely viable but unverified.
When to avoid it — and what to weigh
- High-quality, multilingual output required — Model is optimized for Japanese; no performance data on non-Japanese text. If multilingual generation is critical, consider larger, multilingual alternatives.
- Very long context windows needed — Context length is Unknown and likely limited (standard GPT-NeoX uses ~2k tokens). If document-level or long-form context is essential, verify or use alternatives with explicit context specifications.
- Instruction-following or chat use cases — Model is a base causal LM, not instruction-tuned or chat-aligned. No card evidence of RLHF or instruction datasets. Requires substantial fine-tuning for chatbot-like behavior.
- Real-time, ultra-low-latency inference without acceleration — 203M params on CPU will not meet sub-100ms SLA. Requires GPU and ideally FasterTransformer or quantization to stay performant; adds operational complexity.
License & commercial use
MIT License (OSI-approved, permissive). Permits commercial use, modification, and distribution with attribution and liability disclaimer. No restrictions on commercial deployment stated in card.
MIT is a standard permissive OSI license. Commercial use, commercial deployment, and commercial modification are permitted. No license restrictions exist. However, verify your organization's IP policy and consider data provenance (training data sourced from public CC-100, Wikipedia, MC4); no explicit warranty or liability shield beyond MIT terms is offered.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or adversarial robustness claims in card. Model trained on public web data (CC-100, Wikipedia, MC4); inherits biases and potential harmful content from training corpus. Standard LLM risks: prompt injection, data leakage, jailbreaking not addressed. Self-hosted deployment mitigates some cloud-based risks but requires operator responsibility for input validation and output monitoring. No evidence of red-teaming or safety fine-tuning.
Alternatives to consider
rinna/japanese-gpt2-medium-unidirectional
Alternative Japanese model from same org (rinna); if GPT-2 architecture is acceptable, may have different performance/size trade-offs.
stabilityai/japanese-stablelm-3b-4e1t or larger stablelm variants
Larger, instruction-tuned Japanese models with stronger performance on downstream tasks; trade-off: higher VRAM, more parameters, proprietary-adjacent licensing.
Meta's Llama 2 / Llama 3 (with Japanese tokenizer fine-tuning)
Larger, well-maintained, broader community; requires language-specific adaptation; different license (Llama 2 Community License, not fully open-source).
Ship japanese-gpt-neox-small with senior software developers
japanese-gpt-neox-small offers a permissive MIT license, low resource footprint, and proven integration with production inference engines. Ideal for enterprises needing Japanese-only NLP without external API dependencies. Start with our self-hosted LLM service to integrate and fine-tune in hours.
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japanese-gpt-neox-small FAQ
Can I use this model commercially without restrictions?
What GPU VRAM do I need to run this model?
Is this model suitable for chatbot or instruction-following applications?
What is the context length of this model?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If japanese-gpt-neox-small is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Deploy Japanese AI Locally and Securely
japanese-gpt-neox-small offers a permissive MIT license, low resource footprint, and proven integration with production inference engines. Ideal for enterprises needing Japanese-only NLP without external API dependencies. Start with our self-hosted LLM service to integrate and fine-tune in hours.