Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…
Open-weight model · Text generation
Terminal-12k-bottom80
by Dang Cao Cuong cuong1692001/Terminal-12k-bottom80
Terminal-12k-bottom80 is an open-weight model for text generation from Dang Cao Cuong, released under other. It has 8.2B parameters and a 40,960-token context. At 16-bit it needs about 19.7 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 /helios-storage/helios4-data/cuong/Terminal-complete8k on the nemotroncompletebottom8012k dataset.
Parameters8.2B
Context40,960
Weights16.4 GB
Licenseother
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve Terminal-12k-bottom80 (8.2B 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 | 16.4 GB | 19.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 8.2 GB | 9.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 4.1 GB | 4.9 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.
Terminal-12k-bottom80 on every accelerator the SAVRN Index prices, at every precision
Model Card
This model is a fine-tuned version of /helios-storage/helios4-data/cuong/Terminal-complete8k on the nemotroncompletebottom8012k dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 4 - totaltrainbatchsize: 4 - totalevalbatchsize: 32 - lrschedulertype: cosine - numepochs: 2.0 - Transformers 5.6.0 - Pytorch 2.11.0+cu130 - Datasets 4.0.0 - Tokenizers 0.22.2
Excerpt from the card by Dang Cao Cuong, licensed other.
Configuration
- Architecture
- Qwen3ForCausalLM
- Context length (tokens)
- 40,960
- Layers
- 36
- Hidden size
- 4,096
- Feed-forward size
- 12,288
- Attention heads
- 32
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- Model type
- qwen3
Identity and Version
- Repository
- cuong1692001/Terminal-12k-bottom80
- Publisher
- Dang Cao Cuong
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 8.2B parameters
- Languages
- Not stated by the source
- Revision
- ad74ed9d626c706414768df16337f4b165dab391
- First published
- 2026-09-19
- Last updated
- 2026-09-19
Files and Weights
14 files, 16.4 GB in total. The weights are 2 files totalling 16.4 GB in bin, safetensors.
Weights2 files · 16.4 GB
Configuration5 files · 965.2 KB
Tokenizer2 files · 11.4 MB
Documentation1 file · 1.4 KB
Other3 files · 873.3 KB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 16.4 GB | 62a3635762a3 |
| training_args.bin | Weights | 7.8 KB | 23584c9fd72c |
| all_results.json | Configuration | 246 B | — |
| config.json | Configuration | 1.6 KB | — |
| generation_config.json | Configuration | 187 B | — |
| train_results.json | Configuration | 246 B | — |
| trainer_state.json | Configuration | 962.9 KB | — |
| README.md | Documentation | 1.4 KB | — |
| chat_template.jinja | Other | 4.2 KB | — |
| trainer_log.jsonl | Other | 818.4 KB | — |
| training_loss.png | Other | 50.7 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 11.4 MB | be75606093db |
| tokenizer_config.json | Tokenizer | 720 B | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 16.4 GB
Download from Dang Cao Cuong
Released by Dang Cao Cuong through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 16.4 GB |
| 16-bit | 16.4 GB |
| 8-bit | 8.2 GB |
| 4-bit | 4.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Terminal-12k-bottom80
How much GPU memory does Terminal-12k-bottom80 need?
About 19.7 GB at 16-bit and 4.9 GB at 4-bit: the weights (8.2B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Terminal-12k-bottom80 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 license is Terminal-12k-bottom80 released under?
other, as its publisher declares it. Read the license text before commercial use.
What is Terminal-12k-bottom80's context length?
40,960 tokens, from the maximum position embeddings in its published configuration.
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