Models › Qwen2.5 family

Qwen2.5-1.5B-Instruct

Qwen Apache 2.0 out 2 years ago

1.54B parameters, text in, text out, 32K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #47 in LLMs · score 108.5 method v1 Sources 3 Downloads 30d 4.7M · #33 of 619 tracked

Changed this week: 26 new quantized builds on the Hub (350 in all).2026-10-24 · 22 new quantized builds on the Hub (346 in all).2026-10-24

Can I run it?0.9GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?4.7Mdownloads, 30 days#33 of 619 we track · top 5%+19 likes, downloads −20% this week909 likesHugging Face2026-10-24

Worth reading

Picked by rule from 32 tracked pieces: the lab's own post, the most shared, a field report, the latest.

25 best of 32 tracked

The 2 picks above, plus these 23. 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
  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. ArticleQwen2.5-LLM: Extending the Boundary of LLMs - Alibaba Cloudalibabacloud.com · undated · official
  15. ArticleAlibaba Qwen2.5-Omni-7B: Open Source End-to-End Multimodal AI Modelalizila.com · 2025-03-27
  16. ArticleQwen2.5-VL-32B: Smarter and Lightersimonwillison.net · 2025-03-25
  17. ArticleQwen2.5-LLM: Extending the boundary of LLMs | Qwenqwenlm.github.io · 2024-09-18 · official
  18. ArticleQwen2.5-Max: The Next Leap in AI Innovationmedium.com · 2025-01-29
  19. ArticleQwen2.5-Omni Technical Reporthuggingface.co · 2025-03-30
  20. PaperQwen2.5-Coder Technical Reportarxiv.org · 2024-11-11
  21. DiscussionQwen2.5-coder on the events occurring during the Tiananmen Square protestsnews.ycombinator.com · 2024-11-20
  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

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 (350 quantized builds); a big base for builders (3,764 fine-tunes, adapters and merges).

  • As a service1Featherless
  • On own hardware350quantized builds
  • Built on3,7642,025 fine-tunes · 1,649 adapters · 90 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: pliny-the-prompter/obliteratus ♥ 621 · cvachet/pdf-chatbot ♥ 382 · embedl/hfviewer ♥ 181 · build-small-hackathon/llm-cinema ♥ 110

Will it fit?

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

Fits a 16 GB card at INT4

About 0.9 GB at INT4, 1.9 GB at FP8, 3.7 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.9 GB of weights (estimate; KV cache and activations extra)INT40.9 GBFP8: about 1.9 GB of weights (estimate; KV cache and activations extra)FP81.9 GBBF16: about 3.7 GB of weights (estimate; KV cache and activations extra)BF163.7 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.9 GB of weights (estimate; KV cache and activations extra)INT40.9 GBFP8: about 1.9 GB of weights (estimate; KV cache and activations extra)FP81.9 GBBF16: about 3.7 GB of weights (estimate; KV cache and activations extra)BF163.7 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.

Most coverage came in the first two weeks

Teal is the weights (2024-09-17), amber triangles are 5 dated articles (each links to it), grey ticks are repo commits.

JulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-09-25: 1 repo commit2024-09-19: 1 repo commit2024-09-18: 2 repo commits2024-09-17: 4 repo commitsQwen2.5: A Party of Foundation Models · 2024-09-18Qwen2.5-LLM: Extending the boundary of LLMs | Qwen · 2024-09-18Qwen2.5 Technical Report · 2025-01-03Show HN: I made a free study tool powered by Qwen2.5 · 2025-01-28Qwen2.5-1.5B-Instruct – 128k context, open source | LLM Reference · 2024-06-07Weights 2024-09-17 · 2024-09-17Weights 2024-09-17now · 1 provider
JulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-09-25: 1 repo commit2024-09-19: 1 repo commit2024-09-18: 2 repo commits2024-09-17: 4 repo commitsQwen2.5: A Party of Foundation Models · 2024-09-18Qwen2.5-LLM: Extending the boundary of LLMs | Qwen · 2024-09-18Qwen2.5 Technical Report · 2025-01-03Show HN: I made a free study tool powered by Qwen2.5 · 2025-01-28Qwen2.5-1.5B-Instruct – 128k context, open source | LLM Reference · 2024-06-07Weights 2024-09-17 · 2024-09-17Weights 2024-09-17now · 1 provider

What people built on it

4,114 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

4,114 derivatives on the Hub, led by 2,025 fine-tunes

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

350 quantized (Hugging Face model tree)2,025 fine-tunes (Hugging Face model tree)90 merges (Hugging Face model tree)1,649 adapters (Hugging Face model tree)350 quantized2,025 fine-tunes90 merges1,649 adapters
350 quantized (Hugging Face model tree)2,025 fine-tunes (Hugging Face model tree)90 merges (Hugging Face model tree)1,649 adapters (Hugging Face model tree)350 quantized2,025 fine-tunes90 merges1,649 adapters

All variants →

Reference

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

What the card says

1 cited sentence

Summary generated, cited

Qwen2.5-1.5B-Instruct is a 1.54B parameter causal language model designed for text generation.[1][2]

  1. parameters human
  2. “Type: Causal Language Models”

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: 1,543,714,304
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 Qwen2.5-1.5B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)3.7 GB · Edge (<4 GB)
FP8 (1 byte/param)1.9 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.9 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/Qwen2.5-1.5B (same organization) Family: Qwen/Qwen2.5 4,114 derivatives on the Hub

Base model: Qwen/Qwen2.5-1.5B

quantized: 350finetune: 2,025adapter: 1,649merge: 90

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
MaziyarPanahi/Qwen2.5-1.5B-Instruct-GGUF quantized GGUF — — — MaziyarPanahi 12 11.6M
Qwen/Qwen2.5-1.5B-Instruct-AWQ quantized AWQ 1.78B S apache-2.0 Qwen 12 6.7M
Qwen/Qwen2.5-1.5B-Instruct-GGUF quantized GGUF — — apache-2.0 Qwen 186 3.2M
litert-community/Qwen2.5-1.5B-Instruct finetune — — — apache-2.0 litert-community 57 889.8k
unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit quantized -bnb 1.58B S apache-2.0 unsloth 5 887.9k
unsloth/Qwen2.5-1.5B-Instruct finetune — 1.54B S apache-2.0 unsloth 10 611.4k
unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit quantized -bnb 1.58B S apache-2.0 unsloth 6 408.4k
SakanaAI/TinySwallow-1.5B finetune — 1.54B S apache-2.0 SakanaAI 35 379.8k
bartowski/Qwen2.5-1.5B-Instruct-GGUF quantized GGUF — — apache-2.0 bartowski 23 355.4k
mlc-ai/Qwen2.5-1.5B-Instruct-q4f16_1-MLC quantized — — — — mlc-ai 2 293.1k
Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4 quantized GPTQ 1.54B S apache-2.0 Qwen 9 273.6k
BytedanceDouyinContent/SAIL-VL-1d6-8B finetune — 8.33B M apache-2.0 BytedanceDouyinContent 15 190.9k
SriSanthM/Qwen-1.5B-Tweet-Generations adapter — — — — SriSanthM 2 155.1k
SakanaAI/TinySwallow-1.5B-Instruct finetune — 1.54B S apache-2.0 SakanaAI 61 109.5k
Efficient-Large-Model/Fast_dLLM_v2_1.5B finetune — 1.54B S apache-2.0 Efficient-Large-Model 14 110.5k
ystemsrx/Qwen2.5-Sex adapter GGUF 1.54B S apache-2.0 ystemsrx 208 97.7k
eth-nlped/Qwen2.5-1.5B-pedagogical-rewardmodel finetune — 1.54B S cc-by-4.0 eth-nlped 4 104.2k
JackKozmo29/depguard-jev-style-1.5b finetune — 1.54B S apache-2.0 JackKozmo29 1 68.5k
JackKozmo29/mailguard-jev-style-1.5b finetune — 1.54B S apache-2.0 JackKozmo29 1 68.4k
JackKozmo29/sevclassifier-jev-style-1.5b finetune — 1.54B S apache-2.0 JackKozmo29 1 67.1k
JackKozmo29/codeguard-jev-style-1.5b finetune — 1.54B S apache-2.0 JackKozmo29 1 67k
QuantFactory/Qwen2.5-Sex-GGUF adapter GGUF — — apache-2.0 QuantFactory 35 64.5k
nvidia/Eagle2-2B merge — 2.21B M cc-by-nc-4.0 nvidia 37 64.3k
prismor/prompt-guard-1.5b adapter — 1.54B S apache-2.0 prismor 0 65.9k
katanemo/Arch-Router-1.5B finetune — 1.54B S other katanemo 279 46.3k
csuhan/Tar-1.5B finetune — 2.57B M apache-2.0 csuhan 2 53.8k
Vikhrmodels/Vikhr-Qwen-2.5-1.5B-Instruct finetune — 1.54B S apache-2.0 Vikhrmodels 15 52.3k
lmstudio-community/Qwen2.5-1.5B-Instruct-GGUF quantized GGUF — — apache-2.0 lmstudio-community 2 50.4k
Sedibaai/SedibaLM finetune — 1.54B S cc-by-sa-4.0 Sedibaai 0 47.1k
open-thoughts/OpenThinker3-1.5B finetune — 1.54B S apache-2.0 open-thoughts 16 44.2k
BytedanceDouyinContent/SAIL-VL-1d5-2B finetune — 2.47B M apache-2.0 BytedanceDouyinContent 12 39.8k
SakanaAI/TinySwallow-1.5B-Instruct-GGUF quantized GGUF — — apache-2.0 SakanaAI 30 27.6k
shibing624/chinese-text-correction-1.5b finetune — 1.54B S apache-2.0 shibing624 13 24.9k
onnx-community/Qwen2.5-1.5B-Instruct quantized — — — — onnx-community 6 23.7k
mradermacher/Qwen2.5-1.5B-Instruct-GGUF quantized GGUF — — apache-2.0 mradermacher 1 23k
OpenVINO/Qwen2.5-1.5B-Instruct-int4-ov quantized int4 — — apache-2.0 OpenVINO 2 22.4k
Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int8 quantized GPTQ 1.54B S apache-2.0 Qwen 11 21.3k
BAAI/Aquila-VL-2B-llava-qwen finetune — 2.18B M apache-2.0 BAAI 62 18.1k
legraphista/Qwen2.5-1.5B-Instruct-IMat-GGUF quantized GGUF — — apache-2.0 legraphista 0 19.6k
mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 1 18.7k

Repository changelog

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

Repository changelog 8 commits

DateCommitSHA
2024-09-25 Update metadata: Add library_name to Transformers (#1) 989aa79
2024-09-19 Upload ./tokenizer_config.json with huggingface_hub 5fee7c4
2024-09-18 update README & LICENSE 2fd5061
2024-09-18 Update tokenizer_config.json b9d14db
2024-09-17 Update README.md 582efe6
2024-09-17 Upload folder using huggingface_hub 775b11a
2024-09-17 Create README.md f89e156
2024-09-17 initial commit e52ab42

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Qwen2.5-1.5B-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/Qwen2.5-1.5B-Instruct — — — — — — — live 2026-10-25

Articles and coverage

32 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.

Official3

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.
alibabacloud.comofficialabout the Qwen2.5 family
qwenlm.github.io2024-09-18officialauthorityabout the Qwen2.5 family4,262 words

Analysis6

arxiv.org2025-01-03authorityabout the Qwen2.5 family
databubble.co
q4km.ai
dev.co
freeflashcardgenerator.com2025-01-28about the Qwen2.5 familyHN 3 pts · 0 comments
llmreference.com2024-06-07
All coverage tracked for this model (32) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Qwen2.5-LLM: Extending the Boundary of LLMs - Alibaba Cloudalibabacloud.com official—2026-10-07exa
Qwen2.5-1.5B-Instruct — Benchmarks, Pricing & Downloads - Databubbledatabubble.co—2026-10-07exa
Qwen2.5-1.5B-Instruct by Qwen - Overview, Benchmarks & Hardware | Q4KM.aiq4km.ai—2026-10-07exa
Qwen2.5-1.5B-Instruct: Efficient Open-Source LLM for Chat, Code, and Self-Hosted Deployment | DEV.codev.co—2026-10-07exa
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, exa
Qwen2.5-LLM: Extending the boundary of LLMs | Qwenqwenlm.github.io official2024-09-182026-10-07exa
Qwen2.5-1.5B-Instruct – 128k context, open source | LLM Referencellmreference.com2024-06-072026-10-07exa

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

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  • Qwen2-1.5B · 1.54B
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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:32 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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