gpt-neo-125m
GPT-Neo 125M is a 150M-parameter open-source language model trained by EleutherAI on the Pile dataset. It replicates the GPT-3 architecture at a much smaller scale, making it suitable for text generation tasks on modest hardware. The model is freely available under MIT license with no access gates. It performs better on commonsense reasoning (Winogrande: 51.78%) than on mathematical reasoning (GSM8K: 0.3%), and is best suited for general prompt-based text generation rather than specialized tasks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | EleutherAI |
| Parameters | 150M |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 440.5k |
| Likes | 228 |
| Last updated | 2024-01-31 |
| Source | EleutherAI/gpt-neo-125m |
What gpt-neo-125m is
GPT-Neo 125M is a causal language model (autoregressive) with 150.3M parameters trained on 300B tokens from the Pile using masked autoregressive loss. Training details: 572,300 steps on EleutherAI's infrastructure. Supports multiple frameworks (transformers, PyTorch, JAX, Rust) and serialization formats (safetensors). Context length is not documented. Last model update: 31 January 2024. Evaluated on Open LLM Leaderboard with average score 25.79, indicating entry-level capability for a 125M model.
Run gpt-neo-125m locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="EleutherAI/gpt-neo-125m")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: FP32 inference requires ~600 MB VRAM (150M params × 4 bytes). Practical minimum: 2–4 GB GPU VRAM (accounting for overhead) or CPU with 8+ GB system RAM. Fine-tuning with full precision: 4–8 GB VRAM. LoRA/QLoRA: 2–4 GB VRAM. Verify on target hardware; inference speed depends on framework and batch size.
Model size is well-suited for LoRA and QLoRA fine-tuning on consumer hardware. Transformers library supports standard fine-tuning. No official guidance on LoRA rank/alpha provided in card. EleutherAI has not published LoRA/QLoRA reference implementations for this checkpoint; community implementations exist but require validation. Full fine-tuning is memory-feasible on single GPU.
When to avoid it — and what to weigh
- Requiring high factuality or reasoning — TruthfulQA score (45.58%) and GSM8K score (0.3%) indicate weak mathematical and factual reasoning. Model frequently generates plausible-sounding but incorrect content.
- Production systems with moderation requirements — Model card explicitly warns that Pile dataset contains profanity, lewd language, and biased content. Outputs require human curation before release. No guardrails documented.
- Tasks demanding long-context understanding — Context length is not documented. No evidence of long-range dependency handling. Evaluate empirically for documents exceeding typical 2K token windows.
- Specialized domains (medicine, law, finance) — Pretraining on general Pile dataset provides no domain-specific knowledge. Downstream task performance marked 'TBD' in model card; no specialized evaluations provided.
License & commercial use
MIT license (permissive OSI-approved). Permits unrestricted use, modification, redistribution, and commercial deployment with minimal restrictions (attribution recommended but not legally required).
MIT is a permissive open-source license with no commercial restrictions. You may build commercial products on GPT-Neo 125M without licensing fees or approval. No trademark protection stated. However: (1) Pile dataset contains problematic content (profanity, bias); you are responsible for output validation and user-facing moderation. (2) No warranties provided by EleutherAI. (3) Fact-checking and quality control are your responsibility before production deployment.
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 |
No security audit documented. Model generates text without content filtering; outputs may include harmful, offensive, or false information. Pile training data includes uncurated web text and thus potential adversarial/toxic patterns. Recommend: (1) input sanitization for user-facing prompts, (2) output filtering before public release, (3) rate-limiting if exposed via API, (4) monitoring for prompt injection attacks if integrated with other systems. No differential privacy or robustness guarantees stated.
Alternatives to consider
Pythia (EleutherAI)
Successor to GPT-Neo. Multiple sizes (70M–12B), same org, more recent (2023), includes deduplicated training details and mechanistic interpretability research. Better documentation and community fine-tuning examples.
DistilGPT-2
Even smaller (82M parameters), faster inference on edge, stable API in transformers. Lower capability but proven in constrained environments. Distilled from GPT-2 (well-understood behavior).
OLMo (AI2)
Slightly larger (7B), fully open (weights + training data + code), MIT license. Better eval results and more comprehensive documentation. Trade: 56× more parameters, higher computational cost.
Ship gpt-neo-125m with senior software developers
Start with the Transformers pipeline for quick testing. Plan for output filtering and content moderation before production. Explore Devco's private-LLM and custom-app services for production-grade deployment, fine-tuning, and integration support.
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gpt-neo-125m FAQ
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Software development & web development with DEV.co
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Ready to deploy GPT-Neo 125M?
Start with the Transformers pipeline for quick testing. Plan for output filtering and content moderation before production. Explore Devco's private-LLM and custom-app services for production-grade deployment, fine-tuning, and integration support.