plamo-2-1b
PLaMo 2 1B is a 1-billion-parameter language model from Preferred Networks trained on 4 trillion tokens of English, Japanese, code, and other data. It uses a Samba hybrid architecture (Mamba SSM + sliding-window attention) instead of pure Transformers, targeting efficiency. The model is not instruction-tuned; it performs raw text generation only. Released under Apache 2.0, it is ungated and open for deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | pfnet |
| Parameters | 1.3B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 37.1k |
| Likes | 40 |
| Last updated | 2025-11-07 |
| Source | pfnet/plamo-2-1b |
What plamo-2-1b is
1.3B-parameter causal decoder-only model using Samba architecture (Mamba2 SSM with sliding-window attention, plus normalization layers). Trained on 4T tokens: 45% English, 30% Japanese, 15% code, 10% other (phase 1: 3.5T; phase 2: 0.5T). Context length Unknown. Requires mamba_ssm, causal_conv1d, transformers 4.44–4.57, torch ≤2.5.1, numba-optimized tokenizer. Supports trust_remote_code.
Run plamo-2-1b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="pfnet/plamo-2-1b")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 5–6 GB VRAM for full precision (fp32) inference on a single GPU. Likely reducible to 1–2 GB with 4-bit quantization, but quantization support is not documented. CPU inference feasible for single-token generation. Torch ≤2.5.1 and mamba-specific kernels required; hardware-accelerated SSM support (NVIDIA, AMD) Unknown.
No explicit LoRA or QLoRA documentation in card. Custom_code flag suggests model requires trust_remote_code=True, which may complicate integration with standard fine-tuning frameworks. Instruction-tuning or domain-specific SFT strongly recommended before production use. Feasibility of parameter-efficient tuning (LoRA) requires testing; VRAM for full-parameter fine-tuning Unknown.
When to avoid it — and what to weigh
- Instruction-following or chat applications without further tuning — Model is explicitly not instruction-tuned. Using it raw for assistant-style dialogue will produce unpredictable outputs. Instruction-tuning or full SFT is required before deployment as a chatbot.
- Safety-critical or heavily regulated applications without testing — Card states testing is limited to English/Japanese scenarios only. Bias, harmful outputs, and hallucinations are not characterized. Regulatory compliance (healthcare, finance, legal) requires custom safety evaluation.
- Context-length dependent workloads — Context length is Unknown and not stated in model card. Applications requiring long document reasoning, multi-turn memory, or document retrieval must validate or measure context window empirically.
- Production without quantization on consumer hardware — At 1.3B parameters in float32, unquantized model requires ~5.2 GB VRAM (estimate). Consumer GPUs and edge devices will require 4-bit or 8-bit quantization; feasibility Unknown.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and distribution with attribution and no warranty. No copyleft restrictions.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use. No gating or non-commercial restrictions. Developers may use PLaMo 2 1B in commercial products provided they include the Apache 2.0 license notice. No additional license fees or approval required. Recommended: review Preferred Networks' AI policy (linked in card) for organizational use guidelines.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Model uses trust_remote_code=True, which executes untrusted code from the Hub. Validate model source before production use. Card discloses that testing is limited to English/Japanese; outputs may be biased or inaccurate in unmeasured scenarios. Potential for hallucination and harmful content generation, as with all LLMs. No security audit or adversarial robustness testing mentioned. Recommend threat modeling for your deployment context (e.g., input validation, output filtering, rate limiting).
Alternatives to consider
Mistral 7B
Larger (7B), more mature ecosystem, better-documented fine-tuning support, and clearer inference tooling. Trade-off: higher memory footprint; better for resource-rich deployments.
Phi-3 1B
Similar parameter count (1B), instruction-tuned, strong documentation, native support in vLLM and Ollama. Trade-off: English-only; PLaMo has Japanese parity.
Llama 3.2 1B
1B scale, instruction-tuned, broad serving tool support (llama.cpp, vLLM, TGI), established deployment patterns. Trade-off: Transformer-based (not SSM); PLaMo claims efficiency advantage via Mamba.
Ship plamo-2-1b with senior software developers
PLaMo 2 1B is open-source and ready to integrate. Get started with Transformers or explore custom fine-tuning and deployment options for your specific use case.
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plamo-2-1b FAQ
Can I use PLaMo 2 1B in a commercial product?
What hardware do I need to run this model?
Is this model ready for production as a chatbot or assistant?
What is the context length (maximum tokens I can input)?
Work with a software development agency
Adopting plamo-2-1b 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 an efficient multilingual LLM?
PLaMo 2 1B is open-source and ready to integrate. Get started with Transformers or explore custom fine-tuning and deployment options for your specific use case.