This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
Open-weight model · Text generation
ppt-pythia-1b-appendix-permuted-seed3408-stage2
by Qing Yao qing-yao/ppt-pythia-1b-appendix-permuted-seed3408-stage2
ppt-pythia-1b-appendix-permuted-seed3408-stage2 is an open-weight model for text generation from Qing Yao. It has 1B parameters and a 2,048-token context. At 16-bit it needs about 2.4 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 None. It has been trained using TRL. This model was trained with SFT.
Parameters1B
Context2,048
Weights2.0 GB
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve ppt-pythia-1b-appendix-permuted-seed3408-stage2 (1B 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 | 2.0 GB | 2.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 1.0 GB | 1.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.5 GB | 0.6 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 19, 2026.
Model Card
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
Excerpt from the card by Qing Yao.
Configuration
- Architecture
- GPTNeoXForCausalLM
- Context length (tokens)
- 2,048
- Layers
- 16
- Hidden size
- 2,048
- Feed-forward size
- 8,192
- Attention heads
- 8
- Vocabulary size
- 50,304
- RoPE base
- 10,000
- Model type
- gpt_neox
Identity and Version
- Repository
- qing-yao/ppt-pythia-1b-appendix-permuted-seed3408-stage2
- Publisher
- Qing Yao
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 1B parameters
- Languages
- sft, trl
- Revision
- 0baed911a7ba7a4698860df065d830c5e55b91d2
- First published
- 2026-09-20
- Last updated
- 2026-09-21
Files and Weights
9 files, 2.0 GB in total. The weights are 2 files totalling 2.0 GB in bin, safetensors.
Weights2 files · 2.0 GB
Configuration3 files · 917.4 KB
Tokenizer2 files · 3.6 MB
Documentation1 file · 1.5 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 2.0 GB | 395643bca7b2 |
| training_args.bin | Weights | 6.4 KB | 47a9b91338c0 |
| config.json | Configuration | 773 B | — |
| special_tokens_map.json | Configuration | 585 B | — |
| trainer_state.json | Configuration | 916.1 KB | — |
| README.md | Documentation | 1.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 4.9 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 2.0 GB
Download from Qing Yao
Released by Qing Yao through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 2.0 GB |
| 16-bit | 2.0 GB |
| 8-bit | 1.0 GB |
| 4-bit | 0.5 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About ppt-pythia-1b-appendix-permuted-seed3408-stage2
How much GPU memory does ppt-pythia-1b-appendix-permuted-seed3408-stage2 need?
About 2.4 GB at 16-bit and 0.6 GB at 4-bit: the weights (1B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run ppt-pythia-1b-appendix-permuted-seed3408-stage2 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.
What is ppt-pythia-1b-appendix-permuted-seed3408-stage2's context length?
2,048 tokens, from the maximum position embeddings in its published configuration.
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