Models › Orca-2 family

microsoft Research / non-commercial (microsoft-research-license) out 3 years ago

Text in, text out, 4K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #540 in LLMs · score 75.3 method v1 Sources 3 Downloads 30d 987 · #594 of 619 tracked

Can I use it?Research / non-commercial (microsoft-research-license)Non-commercial onlylicence file2026-10-24
Do people use it?987downloads, 30 days#594 of 619 we track · below the median+2 likes, downloads +9% this week673 likesHugging Face2026-10-24

Worth reading

Picked by rule from 5 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 310 HN pts

Orca 2: Teaching Small Language Models How to Reason

Orca 2 teaches small language models to reason by training them on diverse solution strategies like step-by-step and recall-then-generate rather than just imitating larger models.

arxiv.org 2023-11-21 310 HN pts · 80 comments 453 words about the Orca 2 family family
Latest · 3 years ago

Getting Started with Orca-2-13B

Orca-2-13B is a model available in 7B and 13B parameter versions, fine-tuned from the LLAMA 2 base models to excel in reasoning and math tasks.

  • The installation command explicitly requires the wasmedge_rustls and wasi_nn-ggml plugins to function.
  • The portable Wasm chat application source code is hosted at the LlamaEdge GitHub repository.
secondstate.io 2023-11-27 68 HN pts · 17 comments 625 words

5 best of 5 tracked

The 2 picks above, plus these 3. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleOrca 2: Teaching Small Language Models How to Reasonmicrosoft.com · 2023-11-21 · 8 HN pts · official
  2. ArticleOrca 2: Teaching Small Language Models How to Reasonhuggingface.co · 2023-11-21 · 18 HN pts
  3. ArticleMicrosoft releases Orca 2, two small SLMs that outperform larger counterpartsventurebeat.com · 2023-11-21 · 2 HN pts

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

#Cheapest live rowIn /1MOut /1MContextQuantSeen
1 Featherless — — — — Oct 25

All 1 provider →

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; 11 quantized builds for local use.

  • As a service1Featherless
  • On own hardware11quantized builds
  • Built on105 fine-tunes · 3 adapters · 2 merges
  • In apps59Hugging Face Spaces

Top Spaces running it: ari9dam/Orca-2-13B ♥ 34 · CATIE-AQ/Guide_Evaluation_LLM ♥ 5 · tonic-ai/Orca213B ♥ 3 · li-qing/FIRE ♥ 3

The story so far

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

Most coverage came in the first two weeks

Teal is the weights (2023-11-14), amber triangles are 5 dated articles (each links to it), grey ticks are repo commits.

DecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2023-11-22: 1 repo commit2023-11-21: 3 repo commitsOrca 2: Teaching Small Language Models How to Reason · 2023-11-21Orca 2: Teaching Small Language Models How to Reason · 2023-11-21Getting Started with Orca-2-13B · 2023-11-27Orca 2: Teaching Small Language Models How to Reason · 2023-11-21Microsoft releases Orca 2, two small SLMs that outperform larger counterparts · 2023-11-21Weights 2023-11-14 · 2023-11-14Weights 2023-11-14now · 1 provider
DecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2023-11-22: 1 repo commit2023-11-21: 3 repo commitsOrca 2: Teaching Small Language Models How to Reason · 2023-11-21Orca 2: Teaching Small Language Models How to Reason · 2023-11-21Getting Started with Orca-2-13B · 2023-11-27Orca 2: Teaching Small Language Models How to Reason · 2023-11-21Microsoft releases Orca 2, two small SLMs that outperform larger counterparts · 2023-11-21Weights 2023-11-14 · 2023-11-14Weights 2023-11-14now · 1 provider

What people built on it

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

21 derivatives on the Hub, most of them quantized

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

11 quantized (Hugging Face model tree)5 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)3 adapters (Hugging Face model tree)11 quantized5 fine-tunes2 merges3 adapters
11 quantized (Hugging Face model tree)5 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)3 adapters (Hugging Face model tree)11 quantized5 fine-tunes2 merges3 adapters

All variants →

Reference

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

What the card says

1 cited sentence

Summary generated, cited

Orca 2 is a 13B parameter LlamaForCausalLM model designed for research tasks like reasoning, math problem solving, and text summarization.[1][2][3]

  1. “Orca 2 is a finetuned version of LLAMA-2.”
  2. architectures
  3. “reasoning over user given data, reading comprehension, ma…”

Generated 2026-10-07 15:13 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

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

18 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesLlamaForCausalLM
Model typellama
Dtypefloat32
MoEno
Context length4,096 (config.json max_position_embeddings)
PapersarXiv:2311.11045
LicenseResearch / non-commercial (microsoft-research-license) (other)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · LlamaForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · LlamaForCausalLM · 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.

All variants

21 variants

Lineage

Original model Family: microsoft/Orca-2 21 derivatives on the Hub

finetune: 5quantized: 11merge: 2adapter: 3

Top variants (21 shown, ranked by downloads)

Relation
Quant
21 rows
Variant Relation Quant Params Band License Author Likes Downloads
Locutusque/Orca-2-13b-SFT-v4 finetune — 13.0B L other Locutusque 5 213.3k
Locutusque/Orca-2-13b-SFT_v5 finetune — 13.0B L other Locutusque 1 194.3k
Locutusque/Orca-2-13b-SFT-v6 finetune — 13.0B L other Locutusque 10 188.4k
TheBloke/Orca-2-13B-AWQ quantized AWQ 13.0B L other TheBloke 7 75.4k
TheBloke/Orca-2-13B-GGUF quantized GGUF — — other TheBloke 66 53.4k
tuantran1632001/Psyfighter2-Orca2-13B-ties merge — 13.0B L other tuantran1632001 2 20.9k
second-state/Orca-2-13B-GGUF quantized GGUF — — other second-state 7 20.5k
TheBloke/Orca-2-13B-GPTQ quantized GPTQ 13.0B L other TheBloke 27 15.8k
mradermacher/Orca-2-13b-i1-GGUF quantized GGUF — — other mradermacher 0 8.4k
mradermacher/Orca-2-13b-GGUF quantized GGUF — — other mradermacher 0 7.2k
TachyHealth/Thealth-Microsoft-Orca-2-13b adapter — 13.0B L ms-pl TachyHealth 1 470
ricecake/Orca-2-13B-Pygmalion-LoRA quantized — — — other ricecake 2 307
athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA quantized — — — other athirdpath 2 266
Chat-Error/Testing_orca quantized — — — other Chat-Error 1 232
mgoin/Orca-2-13b-pruned50-quant-ds quantized -quant — — — mgoin 0 269
ssaryssane/ssarry-truthful-13B-slerp merge — 10.5B L — ssaryssane 0 227
itlwas/Orca-2-13b-Q4_K_M-GGUF quantized GGUF — — other itlwas 0 219
ricecake/Orca-2-13B-Pyg-and-Bluemoon adapter — — — — ricecake 3 36
bartowski/Orca-2-13B-no_robots-exl2 finetune exl2 — — other bartowski 1 32
gianlucar/orca2_finetuned_test_contenzioso adapter — — — — gianlucar 0 82
noneUsername/Orca-2-13b-W8A8-Dynamic-Per-Token finetune -W8A8 13.0B L — noneUsername 0 52

Repository changelog

4 commits

Repository changelog 4 commits

DateCommitSHA
2023-11-22 Update README.md (#9) 2539ff5
2023-11-21 Update README.md (#7) 80a5dc4
2023-11-21 Corrected readme (#4) 74f5871
2023-11-21 initial commit 8f65a1a

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Orca-2-13b, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Featherless
hf
microsoft/Orca-2-13b — — — — — — — live 2026-10-25

Articles and coverage

5 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

microsoft.com2023-11-21officialauthorityabout the Orca 2 familyHN 8 pts · 2 comments796 words
Orca 2 comes in two sizes, 7 billion and 13 billion parameters, created by fine-tuning the corresponding LLAMA 2 base models on tailored, high-quality synthetic data. The models attain performance levels similar to or better than models 5-10 times larger as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. Orca 2 models have not gone through reinforcement learning from human feedback (RLHF) training for safety.written, reviewed, cited
  • Orca 2 teaches models to choose different solution strategies like step-by-step processing or direct answer depending on the task.
  • The training data is obtained from a more capable teacher model using very detailed instructions and even multiple calls.
  • Orca 2 models exhibit limitations common to other language models and could retain many of the constraints of the base models.

Analysis4

arxiv.org2023-11-21authorityHN front pageabout the Orca 2 familyHN 310 pts · 80 comments453 words
Orca 2 teaches small language models to reason by training them on diverse solution strategies like step-by-step and recall-then-generate rather than just imitating larger models. The model learns to determine the most effective solution strategy for each task, attaining performance levels similar or better to those of models 5-10x larger. Weights are made publicly available to support research on the development, evaluation, and alignment of smaller LMs.written, reviewed, cited
secondstate.io2023-11-27HN 68 pts · 17 comments625 words
Orca-2-13B is a model available in 7B and 13B parameter versions, fine-tuned from the LLAMA 2 base models to excel in reasoning and math tasks. Running on a Mac M1 with 32G memory, it clocks in at about 9.15 tokens per second using the Rust + Wasm stack. The portable Wasm app automatically takes advantage of hardware accelerators like GPUs on the device.written, reviewed, cited
  • The installation command explicitly requires the wasmedge_rustls and wasi_nn-ggml plugins to function.
  • The portable Wasm chat application source code is hosted at the LlamaEdge GitHub repository.
huggingface.co2023-11-21authorityabout the Orca 2 familyHN 18 pts · 0 comments627 words
Orca 2 enhances smaller language models' reasoning abilities by teaching them diverse solution strategies, outperforming larger models on complex reasoning tasks. The model is evaluated using a comprehensive set of 15 diverse benchmarks corresponding to approximately 100 tasks and over 36,000 unique prompts. Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger in zero-shot settings.written, reviewed, cited
  • The authors contend that excessive emphasis on imitation may restrict the potential of smaller models.
  • Orca 2 teaches the model various reasoning techniques including step-by-step, recall then generate, and direct answer methods.
  • The goal is to help the model learn to determine the most effective solution strategy for each task.
venturebeat.com2023-11-21about the Orca 2 familyHN 2 pts · 0 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).

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:42 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

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