Models › Qwen1.5 family

Qwen1.5-0.5B-Chat

Qwen Research / non-commercial (tongyi-qianwen-research) out 3 years ago

620M parameters, text in, text out, 32K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #316 in LLMs · score 82.7 method v1 Sources 3 Downloads 30d 56.9k · #372 of 619 tracked

Can I run it?0.4GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Research / non-commercial (tongyi-qianwen-research)Non-commercial onlylicence file2026-10-24
Do people use it?56.9kdownloads, 30 days#372 of 619 we track · below the median+3 likes, downloads −9% this week108 likesHugging Face2026-10-24

Worth reading

Picked by rule from 7 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Latest · 3 years ago

Qwen1.5-Moe: Matching 7B Model Performance with 1/3 Activated Parameters

Qwen1.5-MoE-A2.7B matches 7B model performance with only 2.7 billion activated parameters using a 64-expert configuration.

  • The model partitions a single FFN into segments to generate 64 experts without increasing the total parameter count.
  • Training from scratch is inefficient, so the team repurposes the existing Qwen-1.8B model to initialize Qwen1.5-MoE-A2.7B.
qwenlm.github.io 2024-03-28 104 HN pts · 10 comments 1.5k words about the Qwen1.5 family family

7 best of 7 tracked

The 2 picks above, plus these 5. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleQwen1.5-110Bqwenlm.github.io · 2024-04-26 · official
  2. ArticleQwen1.5-32B: Fitting the Capstone of the Qwen1.5 Language Model Seriesqwenlm.github.io · 2024-04-05 · official
  3. ArticleQwen1.5qwenlm.github.io · 2024-02-05 · 5 HN pts · official
  4. DiscussionQwen1.5-110B Previewtwitter.com · 2024-04-20
  5. ArticleRun Alicloud's Qwen LLM with a 2MB cross GPU/CPU rust appsecondstate.io · 2024-02-28

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; 37 quantized builds for local use; a big base for builders (301 fine-tunes, adapters and merges).

  • As a service1Featherless
  • On own hardware37quantized builds
  • Built on30119 fine-tunes · 282 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: lhoestq/fake-data-generator-jsonl ♥ 68 · gsarti/pecore ♥ 18 · seanpedrickcase/Light-PDF-Web-QA-Chatbot ♥ 14 · Justinrune/LLaMA-Factory ♥ 12

Will it fit?

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

Fits a 16 GB card at INT4

About 0.4 GB at INT4, 0.7 GB at FP8, 1.5 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.4 GB of weights (estimate; KV cache and activations extra)INT40.4 GBFP8: about 0.7 GB of weights (estimate; KV cache and activations extra)FP80.7 GBBF16: about 1.5 GB of weights (estimate; KV cache and activations extra)BF161.5 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.4 GB of weights (estimate; KV cache and activations extra)INT40.4 GBFP8: about 0.7 GB of weights (estimate; KV cache and activations extra)FP80.7 GBBF16: about 1.5 GB of weights (estimate; KV cache and activations extra)BF161.5 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.

3 dated articles and 27 repo commits so far

Teal is the weights (2024-01-31), amber triangles are 3 dated articles (each links to it), grey ticks are repo commits.

FebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-04-30: 1 repo commit2024-04-05: 2 repo commits2024-04-04: 1 repo commit2024-02-28: 1 repo commit2024-02-20: 1 repo commit2024-02-09: 1 repo commit2024-02-06: 1 repo commit2024-02-05: 8 repo commits2024-02-03: 2 repo commits2024-02-02: 2 repo commits2024-01-31: 7 repo commitsQwen1.5-Moe: Matching 7B Model Performance with 1/3 Activated Parameters · 2024-03-28Qwen1.5 · 2024-02-05Complete tutorial: Run Qwen LLM across your local machines without Python/C++ · 2024-02-22Weights 2024-01-31 · 2024-01-31Weights 2024-01-31now · 1 provider
FebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-04-30: 1 repo commit2024-04-05: 2 repo commits2024-04-04: 1 repo commit2024-02-28: 1 repo commit2024-02-20: 1 repo commit2024-02-09: 1 repo commit2024-02-06: 1 repo commit2024-02-05: 8 repo commits2024-02-03: 2 repo commits2024-02-02: 2 repo commits2024-01-31: 7 repo commitsQwen1.5-Moe: Matching 7B Model Performance with 1/3 Activated Parameters · 2024-03-28Qwen1.5 · 2024-02-05Complete tutorial: Run Qwen LLM across your local machines without Python/C++ · 2024-02-22Weights 2024-01-31 · 2024-01-31Weights 2024-01-31now · 1 provider

What people built on it

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

338 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.

37 quantized (Hugging Face model tree)19 fine-tunes (Hugging Face model tree)282 adapters (Hugging Face model tree)37 quantized19 fine-tunes282 adapters
37 quantized (Hugging Face model tree)19 fine-tunes (Hugging Face model tree)282 adapters (Hugging Face model tree)37 quantized19 fine-tunes282 adapters

All variants →

Reference

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

What the card says

1 cited sentence

Summary generated, cited

Qwen1.5-0.5B-Chat is a 620M parameter decoder-only language model designed for text-generation tasks.[1][2]

  1. parameters human
  2. “decoder-only language model”

Generated 2026-10-07 15:08 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

22 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesQwen2ForCausalLM
Model typeqwen2
Dtypebfloat16
Params by dtypeBF16: 619,570,176
Size bandS
MoEno
Context length32,768 (config.json max_position_embeddings)
Languagesen
PapersarXiv:2309.16609
LicenseResearch / non-commercial (tongyi-qianwen-research) (other)
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 Qwen1.5-0.5B-Chat: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.5 GB · Edge (<4 GB)
FP8 (1 byte/param)0.7 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.4 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: Qwen/Qwen1.5 338 derivatives on the Hub

quantized: 37finetune: 19adapter: 282

Top variants (40 shown, ranked by downloads)

Relation
Quant
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
Xenova/Qwen1.5-0.5B-Chat quantized — — — — Xenova 9 51.1k
second-state/Qwen1.5-0.5B-Chat-GGUF quantized GGUF — — other second-state 3 16.6k
mlc-ai/Qwen1.5-0.5B-Chat-q4f16_1-MLC quantized — — — — mlc-ai 0 8.9k
Felladrin/gguf-Qwen1.5-0.5B-Chat quantized gguf — — — Felladrin 2 7.6k
DevQuasar-9/Qwen.Qwen1.5-0.5B-Chat-GGUF quantized GGUF — — — DevQuasar-9 1 5k
mradermacher/Qwen1.5-0.5B-Chat-i1-GGUF quantized GGUF — — other mradermacher 0 4.9k
mradermacher/Qwen1.5-0.5B-Chat-GGUF quantized GGUF — — other mradermacher 0 2.9k
Felladrin/gguf-sharded-Qwen1.5-0.5B-Chat quantized gguf — — other Felladrin 0 2.5k
Zynerji/Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored quantized GGUF 464M S apache-2.0 Zynerji 0 2.1k
arzaan789/qwen1.5-0.5b-uncensored quantized GGUF — — apache-2.0 arzaan789 1 1.8k
onnx-community/Qwen1.5-0.5B-Chat-ONNX quantized — — — — onnx-community 0 1.2k
FINGU-AI/Qwen-Orpo-v1 finetune — 464M S apache-2.0 FINGU-AI 0 978
mlc-ai/Qwen1.5-0.5B-Chat-q3f16_1-MLC quantized — — — — mlc-ai 0 801
Elaine5/Qwen1.5-0.5B-Chat-Q4_K_M-GGUF quantized GGUF — — other Elaine5 0 736
mlc-ai/Qwen1.5-0.5B-Chat-q0f16-MLC quantized — — — — mlc-ai 0 406
itlwas/Qwen1.5-0.5B-Chat-Q4_K_M-GGUF quantized GGUF — — other itlwas 0 305
markab/Qwen1.5-Capybara-0.5B-Chat adapter — 464M S other markab 1 226
amai-gsu/Qwen1.5-0.5B-Chat-Q4_K_M-GGUF quantized GGUF — — other amai-gsu 0 275
Yermalovich/results finetune — 464M S other Yermalovich 0 262
attention-avengers/Qwen1.5-0.5B-Chat-EPFL-cDPO adapter — — — — attention-avengers 0 260
panzarasa/qwen1.5-0.5b-websight adapter — 464M S other panzarasa 0 224
Super007/aversi50 finetune — 464M S other Super007 0 197
Anthonyg5005/Qwen1.5-0.5B-Chat-exl2 quantized exl2 — — other Anthonyg5005 0 171
thenewth/Qwen1.5-0.5B-Chat-GrammarCorrection adapter — 464M S apache-2.0 thenewth 0 142
attention-avengers/Qwen1.5-0.5B-Chat-EPFL-ORCA-cDPO adapter — — — — attention-avengers 0 137
nblinh63/d7dec48d-6963-4941-a666-82afe3a01311 adapter — — — other nblinh63 0 131
terry69/qwen_0.5_feedback_dirty finetune — 464M S other terry69 0 126
lescurej/Qwen1.5-0.5B-Chat-Q4_K_M-GGUF quantized GGUF — — other lescurej 0 118
vermoney/968ff187-2ae1-4d9b-9fd9-9af8f08fe27b quantized — — — — vermoney 0 84
chkim123/Qwen1.5-0.5B-Chat-ONNX quantized — — — — chkim123 0 84
vermoney/04ba9e7e-632e-4981-a1dd-9e4604430ba1 quantized — — — — vermoney 0 82
kokovova/a5f58fdb-fe66-4332-bf8f-1066cb6463b7 quantized — — — — kokovova 0 82
dzanbek/c25b500e-4f01-4d5e-b6b8-dbf1683fcb1c quantized — — — — dzanbek 0 78
terry69/qwen_feedback_dirty_0.5 finetune — 464M S other terry69 0 77
Org-Exp/Hyperthink-exp finetune — 494M S — Org-Exp 0 77
mimimimihugh/claim-agent-lora adapter — — — — mimimimihugh 0 76
QINchampion/output finetune — 464M S other QINchampion 0 75
emmabhl/Qwen1.5-0.5B-Chat-ORCA-cDPO adapter — — — — emmabhl 0 71
amai-gsu/Qwen1.5-0.5B-Chat-Q4_K_S-GGUF quantized GGUF — — other amai-gsu 0 71
attention-avengers/Qwen1.5-0.5B-Chat-EPFL-ORCA-DPO adapter — — — — attention-avengers 0 70

Repository changelog

27 commits

Repository changelog 27 commits

DateCommitSHA
2024-04-30 update tokenizer_config.json 4d14e38
2024-04-05 Update README.md f82bd36
2024-04-05 Update README.md 23b9054
2024-04-04 update tokenizer_config.json 2a01ce8
2024-02-28 update generation_config.json 6c70598
2024-02-20 add torch_dtype 7f630fd
2024-02-09 update eos token 81b0090
2024-02-06 update tokenizer_config.json 6114e9c
2024-02-05 update readme 3f5adc4
2024-02-05 update readme 4cbf541
2024-02-05 Update README.md 24a5e87
2024-02-05 update readme 2137f0c
2024-02-05 Update README.md d9f867e
2024-02-05 Update README.md b6eb0ef
2024-02-05 Update README.md e6f98dd
2024-02-05 Update README.md 72bc460
2024-02-03 Update config.json 7ff50b2
2024-02-03 Update config.json 3c812c8
2024-02-02 Update generation_config.json 6c26ede
2024-02-02 Update README.md 5c5635f
2024-01-31 Update README.md 25e33b3
2024-01-31 Update README.md ab0b543
2024-01-31 Update README.md cffcc20
2024-01-31 Update README.md 9e64766
2024-01-31 Create LICENSE 105896a
2024-01-31 Upload folder using huggingface_hub b79d0c9
2024-01-31 initial commit a059837

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Qwen1.5-0.5B-Chat, 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/Qwen1.5-0.5B-Chat — — — — — — — live 2026-10-25

Articles and coverage

7 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.

Official2

qwenlm.github.io2024-03-28officialauthorityHN front pageabout the Qwen1.5 familyHN 104 pts · 10 comments1,495 words
Qwen1.5-MoE-A2.7B matches 7B model performance with only 2.7 billion activated parameters using a 64-expert configuration. The model incorporates 4 shared experts alongside 60 routing experts and requires source installation via git for Hugging Face support. It achieves a 75% decrease in training expenses and accelerates inference speed by a factor of 1.74 compared to Qwen1.5-7B.written, reviewed, cited
  • The model partitions a single FFN into segments to generate 64 experts without increasing the total parameter count.
  • Training from scratch is inefficient, so the team repurposes the existing Qwen-1.8B model to initialize Qwen1.5-MoE-A2.7B.
  • The Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4 quantized variant is available, but AWQ support is currently not supported.
qwenlm.github.io2024-02-05officialauthorityabout the Qwen1.5 familyHN 5 pts · 2 comments3,793 words
Qwen1.5 is a new open-source model series released before the Chinese New Year, offering base and chat models in six sizes ranging from 0.5B to 110B parameters. The Qwen1.5-72B model outperforms Llama2-70B across all benchmarks, and the series features improved multilingual and human-alignment capabilities. All models now uniformly support a context length of up to 32768 tokens, and the Qwen1.5-72B-Chat model is recommended for initial testing.written, reviewed, cited

Analysis1

secondstate.io2024-02-22HN 3 pts · 0 comments629 words
All coverage tracked for this model (7) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Qwen1.5-110Bqwenlm.github.io official2024-04-262026-10-07hn
Qwen1.5-110B Previewtwitter.com2024-04-202026-10-07hn
Qwen1.5-32B: Fitting the Capstone of the Qwen1.5 Language Model Seriesqwenlm.github.io official2024-04-052026-10-07hn
Qwen1.5-Moe: Matching 7B Model Performance with 1/3 Activated Parametersqwenlm.github.io official2024-03-282026-10-07hn
Run Alicloud's Qwen LLM with a 2MB cross GPU/CPU rust appsecondstate.io2024-02-282026-10-07hn
Complete tutorial: Run Qwen LLM across your local machines without Python/C++secondstate.io2024-02-222026-10-07hn
Qwen1.5qwenlm.github.io official2024-02-052026-10-07hn

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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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:38 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:08 UTC · Last verified 2026-10-25

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