Instruction-tuned checkpoint of Han2Han (han2han-ul2-base-1-it, step 43153). Han2Han is a 169M-parameter encoder-decoder model that learns script-invariant representations of Korean text: a document written in Hanja and its Hangul transcription land at the same point in embedding space. The recipe (jamo and character-level embedding fusion, morpheme-aware denoising, bidirectional Hanja-Hangul transcription) is described in the paper, accepted to Findings of EMNLP 2026. This repo holds the PyTorch weights, the SentencePiece tokenizer, and the modeling code needed to load them through the transformers Auto classes with trustremotecode=True. Training code, the Flax model, the Flax-to-PyTorch…
Prochlo-190M-Base is an open-weight model for text generation from CyanoAI, released under Apache License 2.0. It has 190M parameters and a 4,096-token context. At 16-bit it needs about 0.5 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 303 downloads a month.
The smallest and first model from CyanoAI — a 190.5M-parameter dense English base LM. Named after Prochlorococcus, the tiniest and most oxygen-producing photosynthetic organism on Earth: small, dense, and outproducing everything its size.
Parameters190M
Context4,096
Weights380.9 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads303
Runs On
What it takes to serve Prochlo-190M-Base (190M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.4 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
Prochlo-190M-Base on every accelerator the SAVRN Index prices, at every precision
Model Card
By CyanoAI, published under apache-2.0, revision 9c6190d0a075.
The smallest and first model from CyanoAI — a 190.5M-parameter dense English base LM. Named after Prochlorococcus, the tiniest and most oxygen-producing photosynthetic organism on Earth: small, dense, and outproducing everything its size. All variants share the same architecture, tokenizer, and vocabulary, so they are drop-in interchangeable. Training corpus: a curated ~826GB English mixture (web / books / code / reference), stream-packed with the tokenizer jointly shipped in this repo. This is a base model — it speaks fluent Wikipedia-style prose but has not been aligned. For chat or assistant behaviour, start from the SFT or aligned variants. Zero-shot on wikitext (JSON subset, ~500k…
Read CyanoAI's full model card
The smallest and first model from CyanoAI — a 190.5M-parameter dense English base LM. Named after Prochlorococcus, the tiniest and most oxygen-producing photosynthetic organism on Earth: small, dense, and outproducing everything its size.
Model family
| Repo | Stage | Status |
|---|---|---|
CyanoAI/Prochlo-190M-Base |
Pre-trained base | this repo |
CyanoAI/Prochlo-190M-SFT |
Supervised fine-tune | available |
CyanoAI/Prochlo-190M |
Preference-aligned (DPO + RLAIF) | in training |
All variants share the same architecture, tokenizer, and vocabulary, so they are drop-in interchangeable.
Model at a glance
| Parameters | 190.5M dense (untied embeddings) |
| Architecture | LLaMA-style decoder: RMSNorm, RoPE (θ=1e5), SwiGLU, no biases, per-head QK RMSNorm |
| Shape | 12 layers × dim 768, 12 heads, hidden 3072 |
| Vocab | 50,261 (GPT-2 BPE + 4 control tokens) |
| Context | 1024 |
| Precision | FP16 |
| Format | Safetensors, loads with Qwen3ForCausalLM (architecture-compatible; trained from scratch by CyanoAI, not derived from Qwen) |
Pretraining
| Tokens | ~7.7B |
| Steps | 300,000 |
| Optimizer | Muon (2D matrices) + AdamW |
| Schedule | lr 1e-3, warmup 64, linear decay |
| Final train loss | 1.7947 (ppl ≈ 6.0) |
| Final eval loss | 1.6205 (ppl ≈ 5.06, still descending) |
Training corpus: a curated ~826GB English mixture (web / books / code / reference), stream-packed with the tokenizer jointly shipped in this repo.
Intended use
This is a base model — it speaks fluent Wikipedia-style prose but has not been aligned. For chat or assistant behaviour, start from the SFT or aligned variants.
Load
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("CyanoAI/Prochlo-190M-Base")
model = AutoModelForCausalLM.from_pretrained("CyanoAI/Prochlo-190M-Base", dtype="float16")
Evaluation
Zero-shot on wikitext (JSON subset, ~500k tokens): ppl ≈ 109.7.
Not directly comparable to GPT-2-series numbers — different training distribution.
License
Apache 2.0
Configuration
- Context length (tokens)
- 4,096
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Key/value heads
- 12
- Head dimension
- 64
- Vocabulary size
- 50,257
- RoPE base
- 100000
- Model type
- qwen3
Identity and Version
- Repository
- CyanoAI/Prochlo-190M-Base
- Publisher
- CyanoAI
- Task
- Text generation
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 190M parameters
- Languages
- en
- Revision
- 9c6190d0a0755ca77527a3c03a973e938d5d259e
- First published
- 2026-10-05
- Last updated
- 2026-10-07
Files and Weights
5 files, 384.5 MB in total. The weights are 1 file totalling 380.9 MB in safetensors.
Weights1 file · 380.9 MB
Configuration1 file · 1.0 KB
Tokenizer1 file · 3.6 MB
Documentation1 file · 2.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 380.9 MB | 97eaaebe314f |
| config.json | Configuration | 1.0 KB | — |
| README.md | Documentation | 2.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 380.9 MB
Download from CyanoAI
Released by CyanoAI through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 380.9 MB |
| 16-bit | 0.4 GB |
| 8-bit | 0.2 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Derived fromProchlo-190M-SFT
Questions About Prochlo-190M-Base
How much GPU memory does Prochlo-190M-Base need?
About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (190M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Prochlo-190M-Base on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Prochlo-190M-Base commercially?
Yes. Prochlo-190M-Base is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Prochlo-190M-Base's context length?
4,096 tokens, from the maximum position embeddings in its published configuration.
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This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).