pythia-410m
Pythia-410M is a 410-million-parameter open-source language model developed by EleutherAI for interpretability research. It is a causal language model trained on the Pile dataset (825 GiB of diverse English text). The model is not intended for production deployment but can be fine-tuned for research or adapted for downstream tasks. It is available under Apache 2.0 license with no access restrictions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | EleutherAI |
| Parameters | 506M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 278.5k |
| Likes | 38 |
| Last updated | 2023-07-09 |
| Source | EleutherAI/pythia-410m |
What pythia-410m is
Pythia-410M is a GPT-NeoX-based transformer with 24 layers, 1024 model dimension, and 16 attention heads. It has 302.3M non-embedding parameters (505.9M total including embeddings). Trained on the Pile with batch size 2M and learning rate 3.0e-4. Context length is not specified. The model is available in both deduplicated and non-deduplicated variants. 154 intermediate checkpoints per model are hosted on HuggingFace as branches. Requires transformers library and PyTorch for inference.
Run pythia-410m locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="EleutherAI/pythia-410m")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 1.6–2.0 GB VRAM for inference at float32 (505M parameters × 4 bytes). With quantization (int8/float16) approximately 0.8–1.0 GB. Fine-tuning with LoRA feasible on single consumer GPU (8–16 GB). Context length unknown; batch inference scaling cannot be estimated precisely.
Model card explicitly permits fine-tuning under Apache 2.0. Recommends conducting risk and bias assessment on fine-tuned variants. Compatible with HuggingFace Transformers. LoRA/QLoRA feasible given 410M parameter scale. Full fine-tuning batch size depends on available VRAM and unknown context length.
When to avoid it — and what to weigh
- Production chatbot or user-facing application — Model card explicitly states Pythia is not intended for deployment. Has not been fine-tuned with RLHF and will not behave like ChatGPT. May generate harmful or offensive text.
- Factual accuracy or knowledge-heavy tasks — Model was trained to predict next tokens, not to produce accurate information. Trained on Pile which contains biases in gender, religion, and race. Should not be relied upon for factually correct output.
- Non-English or multilingual generation — English-only model. Not suitable for translation or non-English text generation.
- Low-latency serving requirements — 410M parameters requires moderate GPU memory. Unknown context length and no specific serving optimization mentioned beyond endpoints compatibility.
License & commercial use
Licensed under Apache 2.0, an OSI-approved permissive open-source license. Allows commercial use, modification, and distribution with attribution and liability waiver.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use of the model weights. However, model card states the model is not intended for deployment and not a product. Commercial deployment requires your own evaluation of fitness for purpose, risk mitigation (harmful/biased output), and compliance with downstream use context (e.g., GDPR, sector regulations). Consult legal review before productionizing.
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 | Good |
| Assessment confidence | High |
Model trained on Pile dataset, which contains profanity, lewd, and offensive texts. Model card warns it may generate socially unacceptable text even from neutral prompts. No adversarial robustness evaluation mentioned. No built-in content filtering. If deploying output to users, human curation recommended. No claim of security hardening against prompt injection or extraction attacks.
Alternatives to consider
OPT-350M (Meta)
Comparable architecture and parameter count (same non-embedding params as Pythia-410M per card). Broader adoption but closed governance; less suitable for interpretability research.
GPT-Neo 350M (EleutherAI)
Smaller predecessor from same organization. 350M params vs 410M. Still research-focused but older and smaller for modern tasks.
TinyLlama-1.1B (TinyLlama project)
Slightly larger (1.1B) but more recent (2024). Better instruction-following; more suitable for production fine-tuning if you need small-scale deployment.
Ship pythia-410m with senior software developers
Pythia-410M is ideal for interpretability studies and fine-tuning experiments. Start with our private LLM or custom LLM app services to safely deploy and adapt this model. Contact our AI engineers to assess your research or production use case.
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pythia-410m FAQ
Can I use Pythia-410M in a commercial product?
What is the context length of Pythia-410M?
How much GPU memory do I need to run Pythia-410M?
Is Pythia-410M ready for production chatbot or customer-facing applications?
Work with a software development agency
Adopting pythia-410m 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 Use Pythia-410M for Your Research?
Pythia-410M is ideal for interpretability studies and fine-tuning experiments. Start with our private LLM or custom LLM app services to safely deploy and adapt this model. Contact our AI engineers to assess your research or production use case.