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-baseline-seed3407-stage2
by Qing Yao qing-yao/ppt-pythia-1b-baseline-seed3407-stage2
ppt-pythia-1b-baseline-seed3407-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 EleutherAI/pythia-1b. 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-baseline-seed3407-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 Sep 20, 2026.
Model Card
This model is a fine-tuned version of EleutherAI/pythia-1b. 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-baseline-seed3407-stage2
- Publisher
- Qing Yao
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 1B parameters
- Languages
- trl, sft
- Revision
- bfd370e499656e23b40b366c3779c87d24ef39a4
- First published
- 2026-09-19
- Last updated
- 2026-09-20
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 · 768.1 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 | 72c36418d878 |
| training_args.bin | Weights | 6.4 KB | 6a8dca6f8bd3 |
| config.json | Configuration | 773 B | — |
| special_tokens_map.json | Configuration | 585 B | — |
| trainer_state.json | Configuration | 766.7 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.
Built From
- Derived from EleutherAI/pythia-1b
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-baseline-seed3407-stage2
How much GPU memory does ppt-pythia-1b-baseline-seed3407-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-baseline-seed3407-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-baseline-seed3407-stage2's context length?
2,048 tokens, from the maximum position embeddings in its published configuration.
Similar Models
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT.