This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1
Open-weight model · Text generation
ppt-pythia-160m-uniform250-previous_mse-seed208-stage1
by Qing Yao qing-yao/ppt-pythia-160m-uniform250-previous_mse-seed208-stage1
ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 is an open-weight model for text generation from Qing Yao, released under Apache License 2.0. It has 85M parameters and a 2,048-token context. At 16-bit it needs about 0.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset.
Parameters85M
Context2,048
Weights2.9 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 (85M 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 | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 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 7, 2026.
Model Card
By Qing Yao, published under apache-2.0, revision c0c8545d5775.
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1
Read Qing Yao's full model card
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 208 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_min_lr - lr_scheduler_warmup_steps: 13 - training_steps: 250
Training results
Framework versions
- Transformers 5.4.0
- Pytorch 2.8.0+cu128
- Datasets 3.2.0
- Tokenizers 0.22.1
Configuration
- Architecture
- GPTNeoXForCausalLM
- Context length (tokens)
- 2,048
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 10
- Model type
- gpt_neox
Identity and Version
- Repository
- qing-yao/ppt-pythia-160m-uniform250-previous_mse-seed208-stage1
- Publisher
- Qing Yao
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 85M parameters
- Languages
- Not stated by the source
- Revision
- c0c8545d5775ee97dea958ed5d127f94ea27763b
- First published
- 2026-09-25
- Last updated
- 2026-09-25
Files and Weights
66 files, 2.9 GB in total. The weights are 29 files totalling 2.9 GB in bin, pt, pth, safetensors.
Weights29 files · 2.9 GB
Configuration21 files · 21.2 KB
Tokenizer14 files · 25.0 MB
Documentation1 file · 1.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| checkpoint-100/model.safetensors | Weights | 170.2 MB | 8753a568f8fe |
| checkpoint-100/optimizer.pt | Weights | 340.4 MB | a0c67543d3c1 |
| checkpoint-100/rng_state.pth | Weights | 14.7 KB | 2f51e71fc7b1 |
| checkpoint-100/scheduler.pt | Weights | 1.5 KB | 9d7abad781cf |
| checkpoint-100/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| checkpoint-150/model.safetensors | Weights | 170.2 MB | a21cfe5cf88a |
| checkpoint-150/optimizer.pt | Weights | 340.4 MB | 675670e032ad |
| checkpoint-150/rng_state.pth | Weights | 14.7 KB | 2f51e71fc7b1 |
| checkpoint-150/scheduler.pt | Weights | 1.5 KB | a3dffde530f0 |
| checkpoint-150/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| checkpoint-200/model.safetensors | Weights | 170.2 MB | ee262c9e660d |
| checkpoint-200/optimizer.pt | Weights | 340.4 MB | 002ab087afc0 |
| checkpoint-200/rng_state.pth | Weights | 14.7 KB | 2f51e71fc7b1 |
| checkpoint-200/scheduler.pt | Weights | 1.5 KB | aeabaaa65d19 |
| checkpoint-200/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| checkpoint-250/model.safetensors | Weights | 170.2 MB | a00d0ca20cbd |
| checkpoint-250/optimizer.pt | Weights | 340.4 MB | 6c132c45a375 |
| checkpoint-250/rng_state.pth | Weights | 14.7 KB | 2f51e71fc7b1 |
| checkpoint-250/scheduler.pt | Weights | 1.5 KB | 2395199a83e4 |
| checkpoint-250/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| checkpoint-50/model.safetensors | Weights | 170.2 MB | 15749148b0f6 |
| checkpoint-50/optimizer.pt | Weights | 340.4 MB | d03f33810ce0 |
| checkpoint-50/rng_state.pth | Weights | 14.7 KB | 2f51e71fc7b1 |
| checkpoint-50/scheduler.pt | Weights | 1.5 KB | 84927109043e |
| checkpoint-50/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| final/model.safetensors | Weights | 170.2 MB | a00d0ca20cbd |
| final/training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| model.safetensors | Weights | 170.2 MB | a00d0ca20cbd |
| training_args.bin | Weights | 5.3 KB | bfd2747a40ba |
| checkpoint-100/config.json | Configuration | 778 B | — |
| checkpoint-100/generation_config.json | Configuration | 225 B | — |
| checkpoint-100/trainer_state.json | Configuration | 1.3 KB | — |
| checkpoint-150/config.json | Configuration | 778 B | — |
| checkpoint-150/generation_config.json | Configuration | 225 B | — |
| checkpoint-150/trainer_state.json | Configuration | 1.4 KB | — |
| checkpoint-200/config.json | Configuration | 778 B | — |
| checkpoint-200/generation_config.json | Configuration | 225 B | — |
| checkpoint-200/trainer_state.json | Configuration | 1.6 KB | — |
| checkpoint-250/config.json | Configuration | 778 B | — |
| checkpoint-250/generation_config.json | Configuration | 225 B | — |
| checkpoint-250/trainer_state.json | Configuration | 1.7 KB | — |
| checkpoint-50/config.json | Configuration | 778 B | — |
| checkpoint-50/generation_config.json | Configuration | 225 B | — |
| checkpoint-50/trainer_state.json | Configuration | 1.1 KB | — |
| config.json | Configuration | 778 B | — |
| experiment.json | Configuration | 5.1 KB | — |
| final/config.json | Configuration | 778 B | — |
| final/generation_config.json | Configuration | 225 B | — |
| generation_config.json | Configuration | 225 B | — |
| trainer_state.json | Configuration | 2.0 KB | — |
| README.md | Documentation | 1.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| checkpoint-100/tokenizer.json | Tokenizer | 3.6 MB | — |
| checkpoint-100/tokenizer_config.json | Tokenizer | 349 B | — |
| checkpoint-150/tokenizer.json | Tokenizer | 3.6 MB | — |
| checkpoint-150/tokenizer_config.json | Tokenizer | 349 B | — |
| checkpoint-200/tokenizer.json | Tokenizer | 3.6 MB | — |
| checkpoint-200/tokenizer_config.json | Tokenizer | 349 B | — |
| checkpoint-250/tokenizer.json | Tokenizer | 3.6 MB | — |
| checkpoint-250/tokenizer_config.json | Tokenizer | 349 B | — |
| checkpoint-50/tokenizer.json | Tokenizer | 3.6 MB | — |
| checkpoint-50/tokenizer_config.json | Tokenizer | 349 B | — |
| final/tokenizer.json | Tokenizer | 3.6 MB | — |
| final/tokenizer_config.json | Tokenizer | 349 B | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 349 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 2.9 GB
Download from Qing Yao
Released by Qing Yao through its official repository on Hugging Face. Read the license.
Built From
- Derived from EleutherAI/pythia-160m
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 2.9 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About ppt-pythia-160m-uniform250-previous_mse-seed208-stage1
How much GPU memory does ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 need?
About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (85M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 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 ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 commercially?
Yes. ppt-pythia-160m-uniform250-previous_mse-seed208-stage1 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 ppt-pythia-160m-uniform250-previous_mse-seed208-stage1's context length?
2,048 tokens, from the maximum position embeddings in its published configuration.
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