Models › gpt2-medium family

openai-community MIT out 5 years ago

380M parameters, text in, text out, 1K tokens of context.

Verified 2026-10-25 Rank #624 in LLMs · score 73.5 method v1 Sources 3 Downloads 30d 165.1k · #267 of 619 tracked

Can I run it?0.2GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?MITPermissivelicence file2026-10-24
Do people use it?165.1kdownloads, 30 days#267 of 619 we track · top 43%+0 likes, downloads −11% this week207 likesHugging Face2026-10-24

Worth reading

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 15 quantized builds and 100 Spaces.

  • As a service0no API provider listed
  • On own hardware15quantized builds
  • Built on480187 fine-tunes · 293 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: JohnSmith9982/ChuanhuChatGPT ♥ 921 · IndexTeam/IndexTTS-2-Demo ♥ 849 · pliny-the-prompter/obliteratus ♥ 621 · shi-labs/Versatile-Diffusion ♥ 408

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 0.2 GB at INT4, 0.5 GB at FP8, 0.9 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.2 GB of weights (estimate; KV cache and activations extra)INT40.2 GBFP8: about 0.5 GB of weights (estimate; KV cache and activations extra)FP80.5 GBBF16: about 0.9 GB of weights (estimate; KV cache and activations extra)BF160.9 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.2 GB of weights (estimate; KV cache and activations extra)INT40.2 GBFP8: about 0.5 GB of weights (estimate; KV cache and activations extra)FP80.5 GBBF16: about 0.9 GB of weights (estimate; KV cache and activations extra)BF160.9 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 22 repo commits so far

Teal is the weights (2022-03-02), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

JunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-02-19: 1 repo commit2023-06-30: 1 repo commit2023-02-22: 1 repo commit2023-01-24: 1 repo commit2022-08-23: 1 repo commit2022-07-22: 1 repo commit2021-05-21: 2 repo commits2020-12-11: 1 repo commit2020-12-09: 1 repo commit2020-10-12: 1 repo commit2020-05-11: 1 repo commit2020-05-02: 1 repo commit2020-04-24: 1 repo commit2020-01-31: 1 repo commit2019-12-20: 1 repo commit2019-09-23: 1 repo commit2019-05-08: 5 repo commitsHN: 354M LLM with better epistemic calibration than GPT-2 Medium on OOD data · 2026-03-10Weights 2022-03-02 · 2022-03-02Weights 2022-03-02now
JunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-02-19: 1 repo commit2023-06-30: 1 repo commit2023-02-22: 1 repo commit2023-01-24: 1 repo commit2022-08-23: 1 repo commit2022-07-22: 1 repo commit2021-05-21: 2 repo commits2020-12-11: 1 repo commit2020-12-09: 1 repo commit2020-10-12: 1 repo commit2020-05-11: 1 repo commit2020-05-02: 1 repo commit2020-04-24: 1 repo commit2020-01-31: 1 repo commit2019-12-20: 1 repo commit2019-09-23: 1 repo commit2019-05-08: 5 repo commitsHN: 354M LLM with better epistemic calibration than GPT-2 Medium on OOD data · 2026-03-10Weights 2022-03-02 · 2022-03-02Weights 2022-03-02now

What people built on it

495 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

495 derivatives on the Hub, most of them adapters

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

15 quantized (Hugging Face model tree)187 fine-tunes (Hugging Face model tree)293 adapters (Hugging Face model tree)15 quantized187 fine-tunes293 adapters
15 quantized (Hugging Face model tree)187 fine-tunes (Hugging Face model tree)293 adapters (Hugging Face model tree)15 quantized187 fine-tunes293 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

10 rows

Benchmarks 10 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (10)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported 1BW55.72 PPLzero-shotmodel author2026-10-07
author-reported CBT-CN92.35 ACCzero-shotmodel author2026-10-07
author-reported CBT-NE87.1 ACCzero-shotmodel author2026-10-07
author-reported LAMBADA15.6 PPLzero-shotmodel author2026-10-07
author-reported LAMBADA55.48 ACCzero-shotmodel author2026-10-07
author-reported PTB47.33 PPLzero-shotmodel author2026-10-07
author-reported WikiText10326.37 PPLzero-shotmodel author2026-10-07
author-reported WikiText222.76 PPLzero-shotmodel author2026-10-07
author-reported enwiki81.01 BPBzero-shotmodel author2026-10-07
author-reported text81.06 BPCzero-shotmodel author2026-10-07

Use-case evidence

1 use case

What it is good at 1 use case

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

20 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesGPT2LMHeadModel
Model typegpt2
Params by dtypeF32: 379,988,992
Size bandS
MoEno
Context length1,024 (config.json max_position_embeddings)
Languagesen
PapersarXiv:1910.09700
LicenseMIT (mit)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · GPT2LMHeadModel · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · GPT2LMHeadModel · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for gpt2-medium: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)0.9 GB · Edge (<4 GB)
FP8 (1 byte/param)0.5 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.2 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: openai-community/gpt2-medium 495 derivatives on the Hub

finetune: 187quantized: 15adapter: 293

Top variants (40 shown, ranked by downloads)

Relation
Quant
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
nicholasKluge/Aira-2-355M finetune — 355M S apache-2.0 nicholasKluge 2 24.1k
ayjays132/NeuroReasoner-1-NR-1 finetune — 355M S apache-2.0 ayjays132 2 21.7k
mradermacher/gpt2-medium-i1-GGUF quantized GGUF — — mit mradermacher 0 21.7k
mradermacher/gpt2-medium-GGUF quantized GGUF — — mit mradermacher 0 12.5k
MiniLLM/MiniLLM-gpt2-340M finetune — — — apache-2.0 MiniLLM 5 8.1k
DevQuasar-10/openai-community.gpt2-medium-GGUF quantized GGUF — — — DevQuasar-10 1 6.7k
diffusionfamily/diffugpt-m finetune — 355M S apache-2.0 diffusionfamily 0 6.2k
dakwi/chessgpt2-medium-m finetune — 303M S mit dakwi 0 6.2k
raghavbali/gpt2-finetuned-headliner finetune — 355M S mit raghavbali 1 3.7k
trojblue/gpt2-prompt-upscaler-v1 finetune — 355M S mit trojblue 0 3.8k
snehangshu511/gpt2-medium-instruct finetune — 406M S mit snehangshu511 0 3.7k
tensorblock/gpt2-medium-GGUF quantized GGUF — — mit tensorblock 0 3.6k
Alcoft/gpt2-medium-GGUF quantized GGUF — — mit Alcoft 0 3.5k
augustocsc/gpt2_medium_prefix_682k adapter — — — — augustocsc 0 2.7k
augustocsc/gpt2_medium_infix_682k adapter — — — — augustocsc 0 2.5k
dakwi/chessgpt-medium-l finetune — 355M S mit dakwi 0 1.7k
dakwi/chessgpt2-medium-l finetune — 303M S mit dakwi 0 1.4k
OmAhire369/safe-genai-dpo-full finetune — 355M S mit OmAhire369 0 1.3k
pthinc/cicikus_classic quantized GGUF 355M S mit pthinc 0 1.2k
jarod0411/zinc10M_gpt2-medium_SMILES_step1 finetune — 355M S mit jarod0411 0 1.2k
tskow21/gpt2-medium-pii finetune — 355M S mit tskow21 0 1.2k
caseyhahn/gpt2-medium-finetuned-genius-lyrics-updated-data-large finetune — — — mit caseyhahn 0 1k
OmAhire369/safe-genai-ppo-full finetune — 355M S mit OmAhire369 0 977
schaeff/gpt2-medium_LNFree500 finetune — 355M S other schaeff 0 793
IParraMartin/gpt2-medium-bLM100M finetune — 355M S mit IParraMartin 0 793
moos124/julioman122 finetune — 355M S mit moos124 0 693
Sarthak279/Lyrics-Generation finetune — — — mit Sarthak279 1 638
antoste/gpt2-medium-Q4_0-GGUF quantized Q4_0 — — mit antoste 0 680
li-jay-cs/gpt2-medium-supervised-summarize-checkpoint finetune — — — mit li-jay-cs 0 671
Octavian10/my-perfect-gpt2-medium finetune — 355M S mit Octavian10 1 585
TomData/GPT2-review finetune — 355M S — TomData 3 442
Novaciano/GPT2-medium-NSFW_Amoral_Christmas-GGUF quantized GGUF — — mit Novaciano 0 560
cvnberk/gpt2-medium-finetuned-contract-gen finetune — — — mit cvnberk 1 502
sharoz/gpt2-medium-custom-functions-dataset-python finetune — — — mit sharoz 0 547
cwestnedge/gpt2-medium-pubmed finetune — 355M S mit cwestnedge 0 533
SaltyBeach/output finetune — 355M S mit SaltyBeach 0 527
KZMTx/redsolarsky4 finetune — 355M S mit KZMTx 0 518
ijwatson98/sft-gpt2-xsum finetune — 355M S mit ijwatson98 0 512
umaru97/gpt2-product-review-generation finetune — — — mit umaru97 1 417
MiniLLM/SFT-gpt2-340M finetune — — — apache-2.0 MiniLLM 0 460

Repository changelog

22 commits

Repository changelog 22 commits

DateCommitSHA
2024-02-19 Adds the tokenizer configuration file (#13) 6dcaa7a
2023-06-30 Adding ONNX file of this model (#9) f65d496
2023-02-22 Adding `safetensors` variant of this model (#5) 425b0cc
2023-01-24 Adding generation config file(s) a8543b8
2022-08-23 Remove explicit table of contents (#2) e852c90
2022-07-22 Add model card (#1) 8c7ca69
2021-05-21 upload flax model db57b2f
2021-05-21 allow flax 4c9f740
2020-12-11 Migrate model card from transformers-repo 6a81b3f
2020-12-09 For clarity, delete deprecated modelcard.json b9fd73a
2020-10-12 Update tokenizer.json 1538118
2020-05-11 Update config.json 8cc8ed4
2020-05-02 Update rust_model.ot a4b58be
2020-04-24 Update config.json a0d42db
2020-01-31 Update config.json 0a83037
2019-12-20 Update modelcard.json 242f801
2019-09-23 Update tf_model.h5 5117318
2019-05-08 Update config.json 127883f
2019-05-08 Update merges.txt 22651d3
2019-05-08 Update pytorch_model.bin 7b10018
2019-05-08 Update vocab.json e441e78
2019-05-08 initial commit d61fcf1

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency.

Analysis1

truthagi.ai2026-03-10HN 1 pts · 1 comments

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

Similar models

3 models

Similar models

  • SmolLM2-360M · 362M
    same size band (S), same task (text-generation), permissive license
  • LFM2.5-350M · 354M
    same size band (S), same task (text-generation)
  • Qwen2.5-0.5B-Instruct · 494M
    same size band (S), same task (text-generation), more providers, permissive license

Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:41 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:13 UTC · Last verified 2026-10-25

Follow gpt2-medium

Watch this model

Email alerts are not open yet; the feeds above are the same data. See feeds for the item schema.