Model reference · open weights
gpt-neo is an open-weight language model from EleutherAI, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
GPT-Neo 125M Model Description GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model. Training data GPT-Neo 125M was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. Training procedure This model was trained on the Pile for 300 billion tokens over 572,300 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. Eval results TBD Down-Stream Applications TBD BibTeX entry and citation info To cite this model, use Open LLM Leaderboard Evaluation Results Detailed results can be found here
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | EleutherAI |
|---|---|
| Type | Language models |
| Parameters (lead) | 150M |
| Context | 2k tokens |
| Variants | 3 |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 513k downloads / month |
| Likes | 503 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys gpt-neo for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gpt-neo below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-neo","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗