The story so far
Weights, every dated article, and repo commits on one rail.
18 repo commits since the weights landed
Teal is the weights (2022-03-02), grey ticks are repo commits.
all-roberta-large-v1
sentence-transformers Apache 2.0 out 5 years ago
355M parameters, text in, embeddings out, 514 tokens of context. 1 provider serves it.
Verified 2026-10-25 Rank #65 in Embeddings · score 85.8 method v1 Sources 3 Downloads 30d 574.7k · #155 of 619 tracked
Do people use it?574.7kdownloads, 30 days#155 of 619 we track · top 25%66 likesHugging Face2026-10-24
Where to run it
Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.
| # | Cheapest live row | In /1M | Out /1M | Context | Quant | Seen |
|---|---|---|---|---|---|---|
| 1 | HF Inference | — | — | — | — | Oct 25 |
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.
Served by 1 API provider; 1 quantized build for local use.
- As a service1HF Inference
- On own hardware1quantized builds
- Built on1010 fine-tunes · 0 adapters · 0 merges
- In apps43Hugging Face Spaces
Top Spaces running it: pritamdeka/health-article-keyphrase-generator ♥ 8 · aakashaldankar/KaleidoscopeChat ♥ 1 · berndf/EmbeddingVisualizer ♥ 1
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, 0.4 GB at FP8, 0.9 GB at BF16: weights only, so leave room for the KV cache.
Estimate params × bytes per param × 1.2 · how we estimate
What people built on it
11 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.
11 derivatives on the Hub, most of them fine-tunes
From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.
Reference
Everything else, folded: the full tables, every source.
What the card says
1 cited sentence
Summary generated, cited
It maps sentences and paragraphs to a 1024 dimensional dense vector space for tasks like clustering or semantic search.[1]
Generated 2026-10-07 15:04 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.
Use-case evidence
2 use cases
What it is good at 2 use cases
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
21 fields
Listed in Embeddings
Facts
- all-roberta-large-v1 has 355M parameters. (safetensors metadata, observed 2026-10-24)
- Context length is 514 tokens (514). (config.json max_position_embeddings, observed 2026-10-24)
- License: Apache 2.0 (friction: none). Permissive; patent grant. (license file, observed 2026-10-24)
- Created 2022-03-02, last modified 2025-03-06. (HF API, observed 2026-10-24)
| Pipeline tag | sentence-similarity |
|---|---|
| Library | sentence-transformers |
| Architectures | RobertaForMaskedLM |
| Model type | roberta |
| Params by dtype | F32: 355,359,744 · I64: 514 |
| Size band | S |
| MoE | no |
| Context length | 514 (config.json max_position_embeddings) |
| Languages | en |
| Papers | arXiv:1904.06472, arXiv:2102.07033, arXiv:2104.08727, arXiv:1704.05179, arXiv:1810.09305 |
| License | Apache 2.0 (apache-2.0) |
| Hub snapshot | 2026-10-25 |
Servability
vLLM · SGLang
Servability
vLLM: supported · RobertaForMaskedLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · RobertaForMaskedLM · 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 all-roberta-large-v1: 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) | 0.4 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
11 variants
Lineage
finetune: 10quantized: 1
Top variants (11 shown, ranked by downloads)
Repository changelog
18 commits
Repository changelog 18 commits
| Date | Commit | SHA |
|---|---|---|
| 2025-03-06 | Remove deprecated (SEB) evaluation results section | cf74d8a |
| 2024-11-05 | Add exported openvino model 'openvino_model_qint8_quantized.xml' | c8b9f2a |
| 2024-10-11 | Add new SentenceTransformer model with an openvino backend | 953a8f9 |
| 2024-10-11 | Add new SentenceTransformer model with an openvino backend | 21327c3 |
| 2024-10-11 | Add exported ONNX model 'model_qint8_avx512_vnni.onnx' | 52c2fdd |
| 2024-10-11 | Add exported ONNX model 'model_qint8_avx512.onnx' | b6d5cd0 |
| 2024-10-11 | Add exported ONNX model 'model_quint8_avx2.onnx' | 6f0d270 |
| 2024-10-11 | Add exported ONNX model 'model_qint8_arm64.onnx' | a1763a0 |
| 2024-10-11 | Add exported ONNX model 'model_O4.onnx' | c586053 |
| 2024-10-11 | Add exported ONNX model 'model_O3.onnx' | d2b7085 |
| 2024-10-11 | Add exported ONNX model 'model_O2.onnx' | 5641b9d |
| 2024-10-11 | Add exported ONNX model 'model_O1.onnx' | d9b1d34 |
| 2024-10-11 | Add new SentenceTransformer model with an onnx backend | 33e56f5 |
| 2024-03-27 | Adding `safetensors` variant of this model (#3) | 7351068 |
| 2024-02-12 | Update Sentence Transformers metadata (#2) | ace3f8f |
| 2021-08-31 | up | 42d37b9 |
| 2021-08-18 | upload | b6bc5d5 |
| 2021-08-18 | initial commit | 6e727ea |
All providers
1 provider · 1 endpoint
Where to run 1 providers · 1 endpoints
Hosted endpoints observed for all-roberta-large-v1, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting.
| Provider | Model ref | Context | Quant | $/M in | $/M out | tok/s | TTFT | Tools | Status | Observed |
|---|---|---|---|---|---|---|---|---|---|---|
| HF Inference hf | sentence-transformers/all-roberta-large-v1 | — | — | — | — | — | — | — | live | 2026-10-25 |
Articles and coverage
1 piece
Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency.
Papers
5 papers
Papers 5
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).
- Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva +8 · 2019-04-13 · Proceedings of the Workshop on NLP for Conversational AI (2019) · PDF · HF paper page (5 upvotes) · evidence
- Daniel Khashabi, Amos Ng, Tushar Khot +3 · 2021-04-18 · Empirical Methods in Natural Language Processing · PDF · HF paper page (1 upvotes) · evidence
- PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them cited in card peer-reviewed · measuredPatrick Lewis, Yuxiang Wu, Linqing Liu +5 · 2021-02-13 · Transactions of the Association for Computational Linguistics · PDF · HF paper page · evidence
Similar models
3 models
Similar models
- e5-large-v2 · 335M same size band (S), same task (sentence-similarity), more providers, permissive license
- embeddinggemma-300m · 303M same size band (S), same task (sentence-similarity), more providers
- gte-large · 335M same size band (S), same task (sentence-similarity), more providers, permissive license
Sources
3 sources
Sources
Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:37 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:04 UTC · Last verified 2026-10-25
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