Models › TinyLlama-v1.0 family

TinyLlama-1.1B-Chat-v1.0

TinyLlama Apache 2.0 out 3 years ago

1.10B parameters, text in, text out, 2K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #162 in LLMs · score 91.8 method v1 Sources 3 Downloads 30d 964.4k · #116 of 619 tracked

Changed this week: 5 new quantized builds on the Hub (165 in all).2026-10-24

Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?964.4kdownloads, 30 days#116 of 619 we track · top 19%+16 likes, downloads −10% this week1,882 likesHugging Face2026-10-24

Worth reading

Picked by rule from 3 tracked pieces: the lab's own post, the most shared, a field report, the latest.

3 best of 3 tracked

The 2 picks above, plus these 1. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleTinyLlama-1.1B-Chat: Lightweight Open-Source LLM for Edgedev.co · 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 (165 quantized builds); a big base for builders (2,222 fine-tunes, adapters and merges).

  • As a service1Featherless
  • On own hardware165quantized builds
  • Built on2,222585 fine-tunes · 1,605 adapters · 32 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: Vokturz/can-it-run-llm ♥ 1,053 · pliny-the-prompter/obliteratus ♥ 621 · cvachet/pdf-chatbot ♥ 382 · AI-nthusiast/cognitive-proxy ♥ 140

Will it fit?

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

Fits a 16 GB card at INT4

About 0.7 GB at INT4, 1.3 GB at FP8, 2.6 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.7 GB of weights (estimate; KV cache and activations extra)INT40.7 GBFP8: about 1.3 GB of weights (estimate; KV cache and activations extra)FP81.3 GBBF16: about 2.6 GB of weights (estimate; KV cache and activations extra)BF162.6 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.7 GB of weights (estimate; KV cache and activations extra)INT40.7 GBFP8: about 1.3 GB of weights (estimate; KV cache and activations extra)FP81.3 GBBF16: about 2.6 GB of weights (estimate; KV cache and activations extra)BF162.6 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.

1 dated article and 16 repo commits so far

Teal is the weights (2023-12-30), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-03-17: 1 repo commit2024-01-12: 1 repo commit2024-01-02: 3 repo commits2023-12-31: 8 repo commits2023-12-30: 3 repo commitsTinyLlama 1.1B Chat v1.0: benchmarks — ModelCap #170 · 2023-12-30Weights 2023-12-30 · 2023-12-30Weights 2023-12-30now · 1 provider
JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-03-17: 1 repo commit2024-01-12: 1 repo commit2024-01-02: 3 repo commits2023-12-31: 8 repo commits2023-12-30: 3 repo commitsTinyLlama 1.1B Chat v1.0: benchmarks — ModelCap #170 · 2023-12-30Weights 2023-12-30 · 2023-12-30Weights 2023-12-30now · 1 provider

What people built on it

2,387 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

2,387 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.

165 quantized (Hugging Face model tree)585 fine-tunes (Hugging Face model tree)32 merges (Hugging Face model tree)1,605 adapters (Hugging Face model tree)165 quantized585 fine-tunes32 merges1,605 adapters
165 quantized (Hugging Face model tree)585 fine-tunes (Hugging Face model tree)32 merges (Hugging Face model tree)1,605 adapters (Hugging Face model tree)165 quantized585 fine-tunes32 merges1,605 adapters

All variants →

Reference

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

What the card says

1 cited sentence

Summary generated, cited

The model is released under the Apache 2.0 license and follows the Zephyr training recipe.[1][2]

  1. license
  2. “We follow [HF's Zephyr](https://huggingface.co/HuggingFac…”

Generated 2026-10-07 15:03 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

20 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesLlamaForCausalLM
Model typellama
Dtypebfloat16
Params by dtypeBF16: 1,100,048,384
Size bandS
MoEno
Context length2,048 (config.json max_position_embeddings)
Languagesen
LicenseApache 2.0 (apache-2.0)
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.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for TinyLlama-1.1B-Chat-v1.0: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)2.6 GB · Edge (<4 GB)
FP8 (1 byte/param)1.3 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.7 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: TinyLlama/TinyLlama-v1.0 2,387 derivatives on the Hub

quantized: 165adapter: 1,605finetune: 585merge: 32

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF quantized GGUF — — apache-2.0 TheBloke 238 2.4M
TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ quantized GPTQ 1.10B S apache-2.0 TheBloke 14 1.7M
RedHatAI/TinyLlama-1.1B-Chat-v1.0-marlin quantized — 260M S — RedHatAI 2 160.9k
barissglc/tinyllama-tarot-v1 adapter — — — apache-2.0 barissglc 8 107.8k
givyboy/TinyLlama-1.1B-Chat-v1.0-mental-health-conversational adapter — — — apache-2.0 givyboy 9 104.9k
snshrivas10/sft-tiny-chatbot adapter — — — apache-2.0 snshrivas10 0 100k
TheBloke/TinyLlama-1.1B-Chat-v1.0-AWQ quantized AWQ 1.10B S apache-2.0 TheBloke 6 64.4k
OpenVINO/TinyLlama-1.1B-Chat-v1.0-int4-ov quantized int4 — — apache-2.0 OpenVINO 3 45.1k
Edentns/DataVortexTL-1.1B-v0.1 finetune — 1.10B S cc-by-nc-sa-4.0 Edentns 0 44.6k
RedHatAI/TinyLlama-1.1B-Chat-v1.0-pruned2.4 finetune — — — apache-2.0 RedHatAI 2 43.4k
second-state/TinyLlama-1.1B-Chat-v1.0-GGUF quantized GGUF — — apache-2.0 second-state 1 33k
alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2 finetune — 1.10B S apache-2.0 alexredna 1 31.9k
Xenova/TinyLlama-1.1B-Chat-v1.0 quantized — — — — Xenova 8 27.2k
OpenVINO/TinyLlama-1.1B-Chat-v1.0-int8-ov quantized int8 — — apache-2.0 OpenVINO 1 24.6k
mlc-ai/TinyLlama-1.1B-Chat-v1.0-q4f16_1-MLC quantized — — — — mlc-ai 0 21.6k
s3nh/TinyLLama-4x1.1B-MoE merge — 3.38B M mit s3nh 7 20.4k
litert-community/TinyLlama-1.1B-Chat-v1.0 finetune — — — apache-2.0 litert-community 5 18.9k
andrijdavid/tinyllama-dare merge — 1.10B S apache-2.0 andrijdavid 1 13.4k
ShieldX/manovyadh-1.1B-v1-chat finetune — 1.10B S apache-2.0 ShieldX 1 13k
frankenmerger/MiniLlama-1.8b-Chat-v0.1 finetune — 1.89B S apache-2.0 frankenmerger 2 11.3k
hieupt/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF quantized GGUF — — apache-2.0 hieupt 2 9.8k
h4rz3rk4s3/TinyParlaMintLlama-1.1B finetune — 1.10B S apache-2.0 h4rz3rk4s3 0 9.1k
MaziyarPanahi/TinyLlama-1.1B-Chat-v1.0-GGUF quantized GGUF — — apache-2.0 MaziyarPanahi 2 8.9k
davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo finetune — 1.10B S apache-2.0 davanstrien 0 8.3k
QuantFactory/minecraft-GGUF quantized GGUF — — cc QuantFactory 4 7.9k
lxcorp/lambda-1v-1B finetune — 1.10B S mit lxcorp 1 7.8k
alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA finetune — 1.10B S apache-2.0 alexredna 0 7.8k
mlc-ai/TinyLlama-1.1B-Chat-v1.0-q4f32_1-MLC quantized — — — — mlc-ai 0 6.9k
mradermacher/TinyLlama-1.1B-Chat-v1.0-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 1 6.7k
mewaeltsegay/desta_1b finetune — 1.10B S apache-2.0 mewaeltsegay 0 6.5k
pbatra/TinyLlama-1.1B-Chat-v1.0-GGUF quantized GGUF — — apache-2.0 pbatra 0 6.4k
arif-butt/tinyllama-trl-merged adapter — 1.10B S apache-2.0 arif-butt 0 5.8k
arif-butt/tinyllama-peft-merged adapter — 1.10B S apache-2.0 arif-butt 0 5.8k
arif-butt/tinyllama-unsloth-merged adapter — 1.10B S apache-2.0 arif-butt 0 5.7k
h4rz3rk4s3/TinyPoliticaLlama-1.1B finetune — 1.10B S apache-2.0 h4rz3rk4s3 0 5.6k
h4rz3rk4s3/TinyNewsLlama-1.1B finetune — 1.10B S apache-2.0 h4rz3rk4s3 0 5.3k
raidhon/coven_tiny_1.1b_32k_orpo_alpha finetune — 1.10B S apache-2.0 raidhon 2 5k
selinazarzour/healthgpt-tinyllama quantized — 1.13B S apache-2.0 selinazarzour 0 5.1k
Sanjay002/tinyllama-mental-health-finetuned finetune — 1.10B S mit Sanjay002 3 4.6k
premrajreddy/tinyllama-1.1b-home-llm finetune GGUF 1.10B S apache-2.0 premrajreddy 0 4.5k

Repository changelog

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

Repository changelog 16 commits

DateCommitSHA
2024-03-17 Update examples in README to be compatible with soon-to-come ChatWidget (#23) fe8a4ea
2024-01-12 Update README.md (#18) 77e2396
2024-01-02 Add example prompt (#4) de253fa
2024-01-02 Update README.md b3cbbe1
2024-01-02 Upload tokenizer.model af8e934
2023-12-31 Update README.md 44ea18e
2023-12-31 Update README.md 2fef611
2023-12-31 Delete all_results.json 36b9dab
2023-12-31 Delete train_results.json 6302cb1
2023-12-31 Delete training_args.bin ac66911
2023-12-31 Delete trainer_state.json d912882
2023-12-31 Delete runs 11c20b6
2023-12-31 Update config.json 26d45f2
2023-12-30 End of training f14d07e
2023-12-30 Model save 5243d15
2023-12-30 initial commit eb800a9

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for TinyLlama-1.1B-Chat-v1.0, 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
TinyLlama/TinyLlama-1.1B-Chat-v1.0 — — — — — — — live 2026-10-25

Articles and coverage

3 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.

Analysis3

localllms.dev
TinyLlama-1.1B-Chat-v1.0 is a 1.1 billion parameter model trained on datasets including Cerebras/SlimPajama-627B and released in early 2024. It requires approximately 2.2 GB of VRAM when Q4-quantized or around 4.2 GB at full FP16 precision, with an additional 10–20% overhead for KV cache and context length. Although distributed under the apache-2.0 license, developers must verify exact terms on the HuggingFace model card before commercial deployment.written, reviewed, cited
dev.co885 words
TinyLlaama-1.1B-Chat is a 1.1 billion parameter conversational LLM pretrained on 3 trillion tokens under an Apache 2.0 license. It requires approximately 2.2–4.4 GB VRAM for bfloat16 inference, though int8 quantization reduces this to ~1.1–2.2 GB. The model was trained through Sept 2023 and uses the Llama 2 architecture for compatibility with existing tools.written, reviewed, cited
  • StarCoder training data includes GPL code, creating potential downstream licensing risks if the model is bundled with proprietary software.
  • UltraFeedback alignment relies on GPT-4 rankings, meaning OpenAI terms of service may restrict commercial use of derivative outputs.
  • Achieving sub-100ms latencies on standard hardware requires quantization to int8 or int4 alongside specific inference engines like vLLM or llama.cpp.
modelcap.ai2023-12-30

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:35 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:03 UTC · Last verified 2026-10-25

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