llm-jp-4-32b-a3b-thinking
llm-jp-4-32b-a3b-thinking is a 32-billion parameter mixture-of-experts language model developed by Japan's National Institute of Informatics. It supports Japanese and English, uses Apache 2.0 licensing, and is optimized for multi-turn conversational tasks. The model is openly accessible without gating and trained on 11.7T tokens with post-training via supervised fine-tuning and preference optimization.
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 | 32.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 45k |
| Likes | 36 |
| Last updated | 2026-04-24 |
| Source | llm-jp/llm-jp-4-32b-a3b-thinking |
What llm-jp-4-32b-a3b-thinking is
32.1B parameter MoE transformer (128 routed experts, 8 activated per token) with 32 layers, 2,560 hidden size, 40 attention heads, and 65,536 context length. Built on a custom Japanese/English tokenizer (llm-jp-tokenizer v4.0) with unigram byte-fallback. Post-training includes SFT and DPO; pre-training consumed 11.7T tokens across multi-stage pipeline. Designed compatible with OpenAI Harmony response format (with caveats on tokenizer).
Run llm-jp-4-32b-a3b-thinking 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-4-32b-a3b-thinking")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: ~64–128 GB VRAM for fp32 inference (full model); ~16–32 GB for bfloat16 with MoE-aware serving. Activated parameters (~3.8B) enable cheaper inference than dense 32B (e.g., 8–16 GB bfloat16 if MoE routing is optimized in serving framework). Actual requirements depend on batch size, sequence length, and serving software (vLLM, TGI, etc.). Verify with target serving framework.
Model card does not discuss LoRA, QLoRA, or parameter-efficient fine-tuning (PEFT). Post-training used SFT and DPO (full-model), suggesting full-parameter tuning was employed. MoE architecture complicates LoRA (expert selection is not learned via LoRA). For domain adaptation, consider full fine-tuning or data-efficient approaches; PEFT compatibility unknown and requires experimentation.
When to avoid it — and what to weigh
- Extreme Low-Latency Requirements — MoE routing and expert selection add computational overhead. While sparse activation reduces forward-pass cost vs. dense 32B, latency is not optimized for sub-100ms use cases.
- Production Safety-Critical Systems (Without Validation) — Model card explicitly states 'early stages of research and development' with 'not been tuned to ensure outputs align with human intent and safety considerations.' AnswerCarefully scores (3.61–3.70) are modest. Independent safety audits required for regulated domains.
- Limited Compute / Mobile Deployment — 32B total parameters (3.8B activated) still require significant VRAM and compute. Not suitable for edge devices or resource-constrained environments without aggressive quantization.
- Non-English/Japanese Use Cases — Trained and tuned specifically for Japanese and English. Multilingual performance beyond these languages is not documented.
License & commercial use
Apache License, Version 2.0 (OSI-compliant, permissive open-source license). Permits commercial use, modification, and distribution with attribution and license notice. Training corpora include public datasets (llm-jp-corpus-v4.1, llm-jp-corpus-midtraining-v2, NINJAL Web Japanese Corpus) with some portions excluded due to licensing constraints; evaluate corpus licenses if derivative work concerns exist.
Apache 2.0 is a permissive OSI license that explicitly allows commercial use, reproduction, and distribution. No commercial restrictions or usage fees documented. However, verify that training data licensing (particularly excluded corpus portions and NINJAL-derived content) does not impose derivative restrictions relevant to your use case. No warranty or support guarantees provided; production deployment assumes your own risk assessment and legal review.
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 | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model card explicitly notes 'early stages of research' with 'not been tuned to ensure outputs align with human intent and safety considerations.' AnswerCarefully benchmark (Japanese safety) shows modest alignment (3.61–3.70 vs. 4.38–4.43 for GPT-5.4). No red-team results, adversarial robustness testing, or known vulnerability disclosures provided. Recommend independent safety review and prompt injection / jailbreak testing before production use. No cryptographic, authentication, or data-isolation safeguards are LLM-layer concerns; rely on serving infrastructure security.
Alternatives to consider
Qwen/Qwen2.5-32B
Similar parameter count, multilingual (but broader language coverage). Stronger general benchmarks; less Japanese-specific tuning. Commercial support available.
Meta Llama 3.1 (70B)
Larger, more capable, broader community. Higher latency/cost; less tailored to Japanese. Llama 3.1 community quantizations widely available.
OpenAI gpt-4o or gpt-5.4
Superior safety tuning and reasoning (benchmarks show 8.85–8.98 vs. 7.57–7.82). Proprietary; no self-hosting. Suitable if managed inference cost acceptable.
Ship llm-jp-4-32b-a3b-thinking with senior software developers
Start with self-hosted infrastructure on /ai/private-llm, build conversational apps on /ai/custom-llm-apps, or integrate RAG workflows on /ai/rag. Review our model card analysis and run independent safety validation before production. Contact [email protected] for technical questions.
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llm-jp-4-32b-a3b-thinking FAQ
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Custom software development services
Adopting llm-jp-4-32b-a3b-thinking 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 llm-jp-4-32b-a3b-thinking?
Start with self-hosted infrastructure on /ai/private-llm, build conversational apps on /ai/custom-llm-apps, or integrate RAG workflows on /ai/rag. Review our model card analysis and run independent safety validation before production. Contact [email protected] for technical questions.