gpt-neox-japanese-2.7b
gpt-neox-japanese-2.7b is a 2.7 billion parameter Japanese language model trained by ABEJA on public Japanese corpora (CC-100, Wikipedia, OSCAR). It is released under MIT license, ungated, and compatible with Hugging Face Transformers. The model is suitable for Japanese text generation tasks and can be deployed on-premises or in cloud environments.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | abeja |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 99k |
| Likes | 59 |
| Last updated | 2023-04-10 |
| Source | abeja/gpt-neox-japanese-2.7b |
What gpt-neox-japanese-2.7b is
A GPT-NeoX variant fine-tuned for Japanese using a custom BPE tokenizer. Built on Transformers v4.23+, PyTorch-based, supports standard causal language modeling. Training data: Japanese CC-100, Wikipedia, and OSCAR. Context length and exact parameter count (stated as 2.7B) require verification against model card details. Last updated April 2023.
Run gpt-neox-japanese-2.7b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="abeja/gpt-neox-japanese-2.7b")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
Estimated 5–11 GB GPU VRAM (fp32) or 3–6 GB (fp16) for inference. Exact precision and optimization details not stated in model card. Requires verification against actual checkpoint size. CPU-only inference possible but slow; GPU strongly recommended for practical use.
Standard Transformers-compatible architecture supports LoRA and QLoRA fine-tuning. No official LoRA checkpoints or fine-tuning guides provided in model card. Feasibility depends on downstream task data availability and compute. Estimated 8–16 GB VRAM for full fine-tuning; LoRA significantly reduces memory footprint.
When to avoid it — and what to weigh
- High-Quality Multi-Lingual Output Required — Model is Japanese-specialized; performance on English or other languages is not documented and likely degraded.
- Production Systems Needing Latest Training Data — Last modified April 2023; training data recency and potential knowledge cutoff are not stated. Evaluate suitability for time-sensitive applications.
- Strict Safety/Bias Control Needed — No documented safety guardrails, alignment process, or bias evaluation provided. Model card does not address harmful output prevention.
- Enterprise Support Expectations — This is a community-released open-source model. ABEJA does not appear to offer commercial support, SLAs, or maintenance guarantees.
License & commercial use
MIT License (Open Source Initiative approved). Permissive: allows commercial use, modification, and redistribution with attribution. No patent or trademark restrictions stated.
MIT license explicitly permits commercial use, including proprietary deployment and monetized applications. No restrictions on commercial modification or redistribution. No gating, no commercial tier required. However, ABEJA provides no commercial support, SLA, or liability indemnification. Buyers should assume responsibility for model quality, bias, safety, and compliance in production environments.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Stale |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
No documented threat model, adversarial robustness testing, or mitigation for prompt injection, token smuggling, or adversarial inputs. As a base LLM without alignment, it may generate biased, false, or harmful content without guardrails. Recommend input validation, output filtering, and rate-limiting in production systems. Training data (CC-100, Wikipedia, OSCAR) may contain outdated, biased, or sensitive information; no data audits provided. Model checkpoints should be verified for integrity if sourced from untrusted networks.
Alternatives to consider
Llama 2 (Japanese fine-tuned variants)
Larger model family (7B, 13B+) with more recent training; some community Japanese fine-tunes exist. Requires license review for commercial use.
Mistral 7B (Japanese fine-tuned variants)
Apache 2.0 licensed, larger parameter count, faster inference than 2.7B. Japanese variants available; may offer better quality.
rinna/japanese-gpt-1b or similar Japanese-specific models
Alternative Japanese-focused models with varying sizes and training approaches. Evaluate licensing and recency separately.
Ship gpt-neox-japanese-2.7b with senior software developers
gpt-neox-japanese-2.7b offers commercial-friendly licensing and on-premises flexibility for Japanese text generation. Assess your hardware requirements, fine-tuning needs, and alignment constraints, then contact our AI platform team to integrate this model into your infrastructure.
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gpt-neox-japanese-2.7b FAQ
Can I use this model commercially without paying ABEJA or obtaining a separate license?
What GPU is required to run this model?
Is this model updated with recent training data?
Can I fine-tune this model for my Japanese use case?
Custom software development services
Adopting gpt-neox-japanese-2.7b is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Japanese LLM Capabilities?
gpt-neox-japanese-2.7b offers commercial-friendly licensing and on-premises flexibility for Japanese text generation. Assess your hardware requirements, fine-tuning needs, and alignment constraints, then contact our AI platform team to integrate this model into your infrastructure.