gpt-neo-2.7B
GPT-Neo 2.7B is a 2.7-billion-parameter open-source language model trained by EleutherAI on the Pile dataset. It replicates the GPT-3 architecture and excels at text generation from prompts. The model is available under the MIT license with no access restrictions, making it suitable for self-hosted deployment and fine-tuning. Performance benchmarks show it outperforms GPT-2 1.5B and is competitive with GPT-3 Ada on several linguistic and reasoning tasks, though it was last updated in July 2023.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | EleutherAI |
| Parameters | 2.7B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 41.5k |
| Likes | 503 |
| Last updated | 2023-07-09 |
| Source | EleutherAI/gpt-neo-2.7B |
What gpt-neo-2.7B is
GPT-Neo 2.7B is a causal language model (autoregressive transformer) with 2.7B parameters, trained for 420B tokens over 400k steps using cross-entropy loss. It was pre-trained on the Pile, an 800GB curated dataset. The model supports multiple frameworks (PyTorch, JAX, Rust) and formats (Safetensors). Context length is unknown. Evaluated on standard benchmarks: Pile perplexity 5.646, Wikitext-103 PPL 11.39, LAMBADA accuracy 62.22%, Winogrande 56.50%, HellaSwag 42.73%. The model is unquantized by default; quantization/LoRA approaches would reduce footprint.
Run gpt-neo-2.7B 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-2.7B")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 (unverified; user must validate): FP32 inference ~5.4 GB VRAM; FP16 ~2.7 GB VRAM; INT8 quantized ~1.4 GB VRAM. Batch inference and fine-tuning require additional headroom (e.g., 8–16 GB for LoRA fine-tuning on consumer GPUs). CPU inference is feasible for latency-tolerant applications using quantization.
The model is compatible with Hugging Face Transformers, enabling straightforward LoRA and QLoRA fine-tuning via libraries like peft. No official fine-tuning guide provided in the card, but community examples exist. FP16 or INT8 quantization is recommended to fit fine-tuning on 6–8 GB GPUs. Full fine-tuning on a single GPU is feasible but will consume ~8–12 GB VRAM.
When to avoid it — and what to weigh
- Production Systems Requiring High Safety Standards — The model card explicitly warns that GPT-Neo was trained on the Pile, which contains profanity, lewd content, and biased language. No safety filtering is applied; output curation or post-processing is required before public release.
- Strict Low-Latency or Cost-Constrained Inference — At 2.7B parameters, inference latency is measurable on commodity hardware. If sub-100ms response times or minimal VRAM footprint are mandatory, evaluate quantization or distillation first; this model may not be optimal without optimization.
- Tasks Requiring Long-Context Understanding — Context length is unknown and likely limited (GPT-2 era models typically ≤2k tokens). If your use case requires reasoning over long documents or maintaining extensive conversation history, this model may struggle.
- Compliance or Audit-Heavy Regulated Environments — The model was last updated July 2023 with no recent security patches noted. Organizations requiring current threat assessment, model cards with third-party audit trails, or SLAs should verify governance fit.
License & commercial use
MIT License. Permissive OSI-approved license permitting commercial use, modification, distribution, and private use with minimal restrictions (attribution and liability disclaimer required).
MIT is a permissive OSI license explicitly allowing commercial use. You may deploy GPT-Neo 2.7B in commercial products, modify it, and re-distribute under MIT terms. No license-based barrier to commercialization. However, you remain responsible for model output quality, safety filtering, bias mitigation, and compliance with end-user jurisdiction (data residency, export controls, etc.). Consult legal/compliance teams regarding training data (Pile) provenance and liability for generated content.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Stale |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
The model was trained on the Pile, a large-scale internet dataset with known biases and harmful content. Outputs are not filtered and may reflect or amplify training data biases, generate profanity, or produce socially unacceptable text. No model poisoning or adversarial robustness analysis is provided. If deployed in sensitive applications, implement output filtering, human review, and red-teaming. Data residency and privacy: ensure inference infrastructure complies with applicable regulations (e.g., GDPR, HIPAA). No recent security audit or supply-chain verification is documented.
Alternatives to consider
Llama 2 7B (Meta)
Larger (7B vs 2.7B), more recent (2023), community-validated performance, and Llama 2 Community License allows commercial use. Better benchmarks on reasoning tasks. Requires license review for your jurisdiction.
Mistral 7B (Mistral AI)
7B parameters, efficient, open weights, Apache 2.0 license (permissive commercial use). Released 2023, more active maintenance signal. Superior performance on common benchmarks vs GPT-Neo 2.7B.
Pythia 2.8B (EleutherAI)
Same organization, similar scale, released later with more transparent training logs and checkpoints. MIT license. Easier for reproducibility and fine-tuning comparisons if staying within EleutherAI ecosystem.
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gpt-neo-2.7B FAQ
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What is the context length, and can I extend it?
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