Models › Qwen2.5 family

Qwen2.5-0.5B-Instruct

Qwen Apache 2.0 out 2 years ago

494M parameters, text in, text out, 32K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #62 in LLMs · score 105.2 method v1 Sources 3 Downloads 30d 5.3M · #29 of 619 tracked

Changed this week: 19 new quantized builds on the Hub (352 in all).2026-10-24 · 18 new quantized builds on the Hub (351 in all).2026-10-24

Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?5.3Mdownloads, 30 days#29 of 619 we track · top 5%+16 likes, downloads −11% this week710 likesHugging Face2026-10-24

Worth reading

Picked by rule from 31 tracked pieces: the lab's own post, the most shared, a field report, the latest.

25 best of 31 tracked

The 1 pick above, plus these 24. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. Codekev: Jev-like model built on Qwen2.5-0.5Bgithub.com · 2026-09-19 · 3 HN pts
  2. ArticleQwen2.5-VL-32B: Smarter and Lighterqwenlm.github.io · 2025-03-24 · official
  3. ArticleQwen2.5-1M: Deploy your own Qwen with context length up to 1M tokensqwenlm.github.io · 2025-01-26 · official
  4. ArticleQwen2.5: A Party of Foundation Modelsqwenlm.github.io · 2024-09-18 · 168 HN pts · official
  5. ArticleQwen2.5-Max: Exploring the intelligence of large-scale MoE modelqwenlm.github.io · 2025-01-28 · official
  6. ArticleQwen2.5-Coder-32B is an LLM that can code well that runs on my Macsimonwillison.net · 2024-11-13
  7. ArticleShow HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphscinegraphs.ai · 2026-02-08
  8. ArticleQwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1Msimonwillison.net · 2025-01-26
  9. ArticleQwen2.5-Coder Beats GPT-4o and Claude 3.5 Sonnet in Codingqwenlm.github.io · 2024-11-11 · official
  10. ArticleCivic-SLM is a domain-specialized fine-tune of Qwen2.5-7B for U.S. govt dataitsmeduncan.com · 2026-04-25
  11. ArticleQwen2.5-VLqwenlm.github.io · 2025-01-27 · official
  12. CodeQwen2.5-Coder-32B with coding abilities matching those of GPT-4ogithub.com · 2024-11-09
  13. ArticleQwen2.5 Omni: See, Hear, Talk, Write, Do It Allqwenlm.github.io · 2025-03-26 · official
  14. ArticleAlibaba Qwen2.5-Omni-7B: Open Source End-to-End Multimodal AI Modelalizila.com · 2025-03-27
  15. CodeREADME.mdgithub.com · undated
  16. ArticleQwen2.5-VL-32B: Smarter and Lightersimonwillison.net · 2025-03-25
  17. ArticleQwen2.5-Max: The Next Leap in AI Innovationmedium.com · 2025-01-29
  18. ArticleQwen2.5-Omni Technical Reporthuggingface.co · 2025-03-30
  19. PaperQwen2.5-Coder Technical Reportarxiv.org · 2024-11-11
  20. DiscussionQwen2.5-coder on the events occurring during the Tiananmen Square protestsnews.ycombinator.com · 2024-11-20
  21. PaperQwen2.5 Technical Reportarxiv.org · 2025-01-03
  22. CodeQwen2.5-VL: State-of-the-art multimodal LLMgithub.com · 2025-01-30
  23. DiscussionNew Qwen2.5-Max Outperforms DeepSeek V3 in Benchmarkstwitter.com · 2025-01-28
  24. ArticleQwen 2.5-0.5B-Instruct Overviewemergentmind.com · undated

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; heavily run locally (352 quantized builds); a big base for builders (2,049 fine-tunes, adapters and merges).

  • As a service1Featherless
  • On own hardware352quantized builds
  • Built on2,0491,101 fine-tunes · 904 adapters · 44 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: pliny-the-prompter/obliteratus ♥ 621 · allenai/reward-bench ♥ 438 · embedl/hfviewer ♥ 181 · kr-cen/Qwen-Image-MICo ♥ 76

Will it fit?

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

Fits a 16 GB card at INT4

About 0.3 GB at INT4, 0.6 GB at FP8, 1.2 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.3 GB of weights (estimate; KV cache and activations extra)INT40.3 GBFP8: about 0.6 GB of weights (estimate; KV cache and activations extra)FP80.6 GBBF16: about 1.2 GB of weights (estimate; KV cache and activations extra)BF161.2 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.3 GB of weights (estimate; KV cache and activations extra)INT40.3 GBFP8: about 0.6 GB of weights (estimate; KV cache and activations extra)FP80.6 GBBF16: about 1.2 GB of weights (estimate; KV cache and activations extra)BF161.2 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.

5 dated articles and 10 repo commits so far

Teal is the weights (2024-09-16), amber triangles are 5 dated articles (each links to it), grey ticks are repo commits.

OctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-09-25: 1 repo commit2024-09-19: 1 repo commit2024-09-18: 2 repo commits2024-09-17: 4 repo commits2024-09-16: 2 repo commitskev: Jev-like model built on Qwen2.5-0.5B · 2026-09-19Qwen2.5: A Party of Foundation Models · 2024-09-18Show HN: IttyBittyAI – Qwen2.5:0.5B running on a 2017 Asus smartphone · 2026-09-24Qwen2.5 Technical Report · 2025-01-03Show HN: I made a free study tool powered by Qwen2.5 · 2025-01-28Weights 2024-09-16 · 2024-09-16Weights 2024-09-16now · 1 provider
OctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-09-25: 1 repo commit2024-09-19: 1 repo commit2024-09-18: 2 repo commits2024-09-17: 4 repo commits2024-09-16: 2 repo commitskev: Jev-like model built on Qwen2.5-0.5B · 2026-09-19Qwen2.5: A Party of Foundation Models · 2024-09-18Show HN: IttyBittyAI – Qwen2.5:0.5B running on a 2017 Asus smartphone · 2026-09-24Qwen2.5 Technical Report · 2025-01-03Show HN: I made a free study tool powered by Qwen2.5 · 2025-01-28Weights 2024-09-16 · 2024-09-16Weights 2024-09-16now · 1 provider

What people built on it

2,401 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

2,401 derivatives on the Hub, led by 1,101 fine-tunes

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

352 quantized (Hugging Face model tree)1,101 fine-tunes (Hugging Face model tree)44 merges (Hugging Face model tree)904 adapters (Hugging Face model tree)352 quantized1,101 fine-tunes44 merges904 adapters
352 quantized (Hugging Face model tree)1,101 fine-tunes (Hugging Face model tree)44 merges (Hugging Face model tree)904 adapters (Hugging Face model tree)352 quantized1,101 fine-tunes44 merges904 adapters

All variants →

Reference

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

What the card says

2 cited sentences

Summary generated, cited

Qwen2.5-0.5B-Instruct is a 494M parameter instruction-tuned causal language model designed for text generation.[1][2] It supports a context length of 32,768 tokens and can generate up to 8,192 tokens.[3]

  1. parameters human
  2. “Type: Causal Language Models”
  3. “Full 32,768 tokens and generation 8192 tokens”

Generated 2026-10-07 15:00 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

4 use cases

What it is good at 4 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

22 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesQwen2ForCausalLM
Model typeqwen2
Dtypebfloat16
Params by dtypeBF16: 494,032,768
Size bandS
MoEno
Context length32,768 (config.json max_position_embeddings)
Languagesen
PapersarXiv:2407.10671
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Qwen2ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Qwen2ForCausalLM · 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 Qwen2.5-0.5B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.2 GB · Edge (<4 GB)
FP8 (1 byte/param)0.6 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.3 GB · Edge (<4 GB)

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

All variants

40 variants

Lineage

Fine-tuned from Qwen/Qwen2.5-0.5B (same organization) Family: Qwen/Qwen2.5 2,401 derivatives on the Hub

Base model: Qwen/Qwen2.5-0.5B

quantized: 352finetune: 1,101merge: 44adapter: 904

Top variants (40 shown, ranked by downloads)

Relation
Quant
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
livekit/turn-detector quantized — 135M S other livekit 166 6.4M
unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit quantized -bnb 505M S apache-2.0 unsloth 4 2.4M
Qwen/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 Qwen 157 1.9M
mlc-ai/Qwen2.5-0.5B-Instruct-q4f16_1-MLC quantized — — — — mlc-ai 4 1.7M
unsloth/Qwen2.5-0.5B-Instruct finetune — 494M S apache-2.0 unsloth 14 1.5M
unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit quantized -bnb 504M S apache-2.0 unsloth 6 716.1k
OpenGVLab/InternVL2_5-1B merge — 938M S mit OpenGVLab 71 564.2k
bartowski/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 bartowski 19 447.6k
OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc quantized int4 184M S apache-2.0 OPEA 0 188.8k
onnx-community/Qwen2.5-0.5B-Instruct quantized — — — — onnx-community 20 186.3k
Qwen/Qwen2.5-0.5B-Instruct-AWQ quantized AWQ 630M S apache-2.0 Qwen 17 159.7k
lmstudio-community/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 lmstudio-community 8 139k
manycore-research/SpatialLM-Qwen-0.5B finetune — 505M S cc-by-nc-4.0 manycore-research 94 121.5k
taobao-mnn/Qwen2.5-0.5B-Instruct-MNN finetune — — — apache-2.0 taobao-mnn 4 121.9k
lmstudio-community/Qwen2.5-0.5B-Instruct-MLX-4bit quantized MLX 494M S apache-2.0 lmstudio-community 1 115.1k
Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int4 quantized GPTQ 494M S apache-2.0 Qwen 15 114.3k
manycore-research/SpatialLM1.1-Qwen-0.5B finetune — 604M S cc-by-nc-4.0 manycore-research 38 105.7k
litert-community/Qwen2.5-0.5B-Instruct finetune — — — apache-2.0 litert-community 9 77.9k
Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8 quantized GPTQ 494M S apache-2.0 Qwen 17 62.1k
ReDiX/Qwen2.5-0.5B-Instruct-ITA finetune — — — apache-2.0 ReDiX 3 47.1k
madhurjindal/Jailbreak-Detector-2-XL adapter — — — mit madhurjindal 17 42.3k
Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct finetune — 494M S apache-2.0 Vikhrmodels 19 34.3k
lightblue/lb-reranker-0.5B-v1.0 finetune — 494M S apache-2.0 lightblue 76 30.3k
nvidia/Eagle2-1B merge — 1.06B S cc-by-nc-4.0 nvidia 33 27.8k
legraphista/Qwen2.5-0.5B-Instruct-IMat-GGUF quantized GGUF — — apache-2.0 legraphista 0 27.1k
IAAR-Shanghai/xVerify-0.5B-I finetune — 494M S cc-by-nc-nd-4.0 IAAR-Shanghai 11 21.8k
gaianet/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 gaianet 0 21.8k
mlc-ai/Qwen2.5-0.5B-Instruct-q4f32_1-MLC quantized — — — — mlc-ai 0 21.5k
KingNish/Qwen2.5-0.5b-Test-ft finetune — 494M S apache-2.0 KingNish 12 20k
wuyanzu4692/task-13-Qwen-Qwen2.5-0.5B-Instruct adapter — — — — wuyanzu4692 0 19.4k
Ihor/Text2Graph-R1-Qwen2.5-0.5b finetune — 494M S apache-2.0 Ihor 24 17.9k
second-state/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 second-state 2 17.4k
mradermacher/Qwen2.5-0.5B-Instruct-GGUF quantized GGUF — — apache-2.0 mradermacher 0 15.6k
yiyuliu/voice-command-intent-qwen0.5b adapter GGUF — — apache-2.0 yiyuliu 0 14.4k
mradermacher/Qwen2.5-0.5B-Instruct-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 2 14.1k
furiosa-ai/Qwen2.5-0.5B-Instruct finetune — — — apache-2.0 furiosa-ai 0 13.3k
Hugggme/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF quantized GGUF — — apache-2.0 Hugggme 26 10.5k
sallani/EUAIAct-Qwen2.5-0.5B-Edge adapter GGUF 494M S apache-2.0 sallani 2 11.5k
reaperdoesntknow/DeepReasoning_1R finetune — 494M S — reaperdoesntknow 2 11.4k
saidutta69/Qwen2.5-0.5B-Instruct-heretic quantized GGUF 494M S other saidutta69 0 11k

Repository changelog

10 commits
changed in the last 7 daysOther: 2026-10-24, 2026-10-24all changes

Repository changelog 10 commits

DateCommitSHA
2024-09-25 Update metadata: Add library_name to Transformers (#2) 7ae5576
2024-09-19 Upload ./tokenizer_config.json with huggingface_hub a8b602d
2024-09-18 update README & LICENSE f0ef055
2024-09-18 Update tokenizer_config.json a338b55
2024-09-17 Update README.md d955144
2024-09-17 Update README.md be08fe9
2024-09-17 Update README.md 361e0b3
2024-09-17 Create README.md a0f8e5d
2024-09-16 Upload folder using huggingface_hub eaa56b5
2024-09-16 initial commit 4a7e54c

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Qwen2.5-0.5B-Instruct, 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
Qwen/Qwen2.5-0.5B-Instruct — — — — — — — live 2026-10-25

Articles and coverage

31 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

qwenlm.github.io2024-09-18officialauthorityHN front pageabout the Qwen2.5 familyHN 168 pts · 38 comments1,744 words
Qwen2.5 introduces dense decoder-only models pretrained on up to 18 trillion tokens, available in sizes from 0.5B to 72B parameters. Most variants are licensed under Apache 2.0, excluding the 3B and 72B models, and support a 128K context window with 8K generation limits. Specialized versions for coding and mathematics offer enhanced performance on benchmarks like HumanEval and MATH.written, reviewed, cited
  • Qwen2.5-Coder-7B-Instruct outperforms many larger language models across a range of programming languages and tasks.
  • Qwen2.5-Math-72B-Instruct general performance surpasses both Qwen2-Math-72B-Instruct and GPT4-o.

Analysis7

github.com2026-09-19authorityHN 3 pts · 0 comments6,746 words
Kev is a family of small decision models built on Qwen3.5 and Qwen3.8 that you can train and run locally. The API matches TypeSafe's System One, so you can point their Python SDK at your local server. The models support yes/no, multiple-choice, and rating questions in one request, with the 27B variant shipping as 51 GB of full weights.written, reviewed, cited
  • Kev-27B requires 96–128 GB of RAM on Mac, a requirement marked as expected but not measured.
  • The Kev-27B model ships as 51 GB of full weights because every weight is fine-tuned rather than using an adapter.
  • An explicitly requested accelerator fails instead of falling back if the probe times out or crashes.
github.comauthorityabout the Qwen2.5 family
arxiv.org2025-01-03authorityabout the Qwen2.5 family
emergentmind.com
llm-explorer.com
emergentmind.comabout the Qwen2.5 family
freeflashcardgenerator.com2025-01-28about the Qwen2.5 familyHN 3 pts · 0 comments

Community1

news.ycombinator.com2026-09-24about the Qwen2.5 familyHN 2 pts · 0 comments494 words
All coverage tracked for this model (31) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
README.mdgithub.com—2026-10-07exa
Qwen 2.5-0.5B-Instruct Overviewemergentmind.com—2026-10-07exa
Qwen2.5 0.5B Instruct by Qwen — VRAM 1GB, 32K context | LLM Explorerllm-explorer.com—2026-10-07exa
Qwen2.5-Instruct: Advanced Instruction LLMemergentmind.com—2026-10-07exa
Show HN: IttyBittyAI – Qwen2.5:0.5B running on a 2017 Asus smartphonenews.ycombinator.com2026-09-242026-10-07hn
kev: Jev-like model built on Qwen2.5-0.5Bgithub.com2026-09-192026-10-07hn
Civic-SLM is a domain-specialized fine-tune of Qwen2.5-7B for U.S. govt dataitsmeduncan.com2026-04-252026-10-07hn
Show HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphscinegraphs.ai2026-02-082026-10-07hn
Qwen2.5-Omni Technical Reporthuggingface.co2025-03-302026-10-07hn
Alibaba Qwen2.5-Omni-7B: Open Source End-to-End Multimodal AI Modelalizila.com2025-03-272026-10-07hn
Qwen2.5 Omni: See, Hear, Talk, Write, Do It Allqwenlm.github.io official2025-03-262026-10-07hn
Qwen2.5-VL-32B: Smarter and Lightersimonwillison.net2025-03-252026-10-07hn
Qwen2.5-VL-32B: Smarter and Lighterqwenlm.github.io official2025-03-242026-10-07hn
Qwen2.5-Pluschat.qwenlm.ai2025-01-312026-10-07hn
Qwen2.5-VL: State-of-the-art multimodal LLMgithub.com2025-01-302026-10-07hn
Qwen2.5-Max: The Next Leap in AI Innovationmedium.com2025-01-292026-10-07hn
New Qwen2.5-Max Outperforms DeepSeek V3 in Benchmarkstwitter.com2025-01-282026-10-07hn
Qwen2.5-Max: Exploring the intelligence of large-scale MoE modelqwenlm.github.io official2025-01-282026-10-07hn
Show HN: I made a free study tool powered by Qwen2.5freeflashcardgenerator.com2025-01-282026-10-07hn
Qwen2.5-VLqwenlm.github.io official2025-01-272026-10-07hn
Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1Msimonwillison.net2025-01-262026-10-07hn
Qwen2.5-1M: Deploy your own Qwen with context length up to 1M tokensqwenlm.github.io official2025-01-262026-10-07hn
Qwen2.5 Technical Reportarxiv.org2025-01-032026-10-07exa
Qwen2.5-coder on the events occurring during the Tiananmen Square protestsnews.ycombinator.com2024-11-202026-10-07hn
Qwen2.5-Coder-32B is an LLM that can code well that runs on my Macsimonwillison.net2024-11-132026-10-07hn
Qwen2.5-Coder Technical Reportarxiv.org2024-11-112026-10-07hn
Qwen2.5 Coderollama.com2024-11-112026-10-07hn
Qwen2.5-Coder Beats GPT-4o and Claude 3.5 Sonnet in Codingqwenlm.github.io official2024-11-112026-10-07hn
Qwen2.5-Coder-32B with coding abilities matching those of GPT-4ogithub.com2024-11-092026-10-07hn
Guide how to use a local LLM Qwen2.5-coder-7B as an autocompelte in VSCodeeamag.me2024-10-292026-10-07hn
Qwen2.5: A Party of Foundation Modelsqwenlm.github.io official2024-09-182026-10-07hn

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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3 models

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  • Qwen2-0.5B-Instruct · 494M
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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:33 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:00 UTC · Last verified 2026-10-25

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