typhoon2.5-qwen3-4b
Typhoon2.5-Qwen3-4B is a 4-billion-parameter Thai/English language model maintained by Typhoon AI. It offers 256K context length, function-calling support, and is based on Qwen3 architecture. The model is ungated, licensed under Apache 2.0, and can run on modest hardware. It is positioned for Thai-language tasks and conversational applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | typhoon-ai |
| Parameters | 4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 102.3k |
| Likes | 6 |
| Last updated | 2026-06-11 |
| Source | typhoon-ai/typhoon2.5-qwen3-4b |
What typhoon2.5-qwen3-4b is
A 4B-parameter decoder-only transformer (Qwen3-based) with 256K context window and built-in function-calling. Requires transformers ≥4.51.0. Supports bfloat16 inference and is compatible with vLLM for OpenAI-compatible API serving. Chat-templated and instruction-tuned. Designed for Thai (primary) and English (secondary) languages.
Run typhoon2.5-qwen3-4b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="typhoon-ai/typhoon2.5-qwen3-4b")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: ~8 GB VRAM for bfloat16 inference (4B params × 2 bytes + KV cache + overhead). Lower with int8 quantization (~4–6 GB). CPU inference not recommended. Specific throughput/latency benchmarks not provided in documentation.
LoRA/QLoRA feasibility is plausible given 4B base size and transformers library integration, but no explicit fine-tuning guidance, adapter support, or benchmarks are documented. Requires custom setup and validation.
When to avoid it — and what to weigh
- Require high-quality English-only performance — Model is optimized for Thai; English performance is secondary and may lag behind larger or English-specialized models.
- Need strict safety guarantees or zero-trust environments — Model card acknowledges guardrails are still in development and may produce inaccurate, biased, or objectionable outputs. Requires additional validation in sensitive use cases.
- Cannot allocate GPU memory or need edge inference on CPU only — 4B model in bfloat16 requires GPU; CPU-only inference is not documented and will be slow or infeasible.
- Require long-term commercial support or SLA guarantees — Community-driven development via Discord; no formal support agreement or guaranteed uptime.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Allows commercial use, modification, and redistribution with attribution and liability disclaimer. No restrictions on proprietary applications.
Apache 2.0 explicitly permits commercial use and closed-source applications without royalty. However, model card directs users to agree to OpenTyphoon Terms and Conditions (https://opentyphoon.ai/tac) and Privacy Notice. These external terms may impose additional restrictions or data-sharing obligations beyond the license itself. Recommend explicit legal review of ToS/Privacy policies before deploying in production.
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 card acknowledges guardrails are under development and may produce inaccurate, biased, or objectionable outputs. No security audit, red-teaming report, or adversarial robustness data provided. Ungated and publicly available, so no access control. Organizations should implement input filtering, output validation, and monitoring for sensitive applications. OpenTyphoon Terms of Conditions should be reviewed for data privacy and usage terms.
Alternatives to consider
Qwen3-4B-Instruct
Base model; may have broader language support and no external ToS requirements, but lacks Thai optimization.
Llama 3.2-1B or 3B
Smaller, widely optimized for English/multilingual, extensive community support. Trade-off: weaker Thai performance.
mT5 or mBERT (encoder-only)
If multilingual classification/NLU is sufficient instead of generation; lighter and faster on CPU.
Ship typhoon2.5-qwen3-4b with senior software developers
Review our detailed technical assessment to determine if Typhoon2.5-Qwen3-4B fits your private LLM, custom app, or RAG project. Check hardware requirements, licensing terms, and deployment complexity before committing. Contact our team for architectural guidance.
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typhoon2.5-qwen3-4b FAQ
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Custom software development services
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 typhoon2.5-qwen3-4b is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Evaluate Typhoon2.5-Qwen3-4B for Your AI Initiative
Review our detailed technical assessment to determine if Typhoon2.5-Qwen3-4B fits your private LLM, custom app, or RAG project. Check hardware requirements, licensing terms, and deployment complexity before committing. Contact our team for architectural guidance.