Open-weight model
qwen2.5-7b-fve-advanchor-s0
by Joshua Fonseca joshycodes/qwen2.5-7b-fve-advanchor-s0
qwen2.5-7b-fve-advanchor-s0 is an open-weight model from Joshua Fonseca, released under other. It has 7.6B parameters and a 32,768-token context. At 16-bit it needs about 18.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,616,679 tokens, 7,827 documents, of which 0 self-authored and 7,827 ordinary text) on a corpus the model wrote for the training of the next version of…
Runs On
What it takes to serve qwen2.5-7b-fve-advanchor-s0 (7.6B 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 | 15.2 GB | 18.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 7.6 GB | 9.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 3.8 GB | 4.6 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.
qwen2.5-7b-fve-advanchor-s0 on every accelerator the SAVRN Index prices, at every precision
Model Card
Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,616,679 tokens, 7,827 documents, of which 0 self-authored and 7,827 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy.
Excerpt from the card by Joshua Fonseca, licensed other.
Configuration
- Architecture
- Qwen2ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 28
- Hidden size
- 3,584
- Feed-forward size
- 18,944
- Attention heads
- 28
- Key/value heads
- 4
- Vocabulary size
- 152,064
- Stored precision
- bfloat16
- Model type
- qwen2
Identity and Version
- Repository
- joshycodes/qwen2.5-7b-fve-advanchor-s0
- Publisher
- Joshua Fonseca
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 7.6B parameters
- Languages
- Not stated by the source
- Revision
- c60e12ae3fc12f9ac310456f0f29519aa218e73d
- First published
- 2026-09-29
- Last updated
- 2026-09-29
Files and Weights
12 files, 15.2 GB in total. The weights are 1 file totalling 15.2 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 15.2 GB | 9cbb5a1e75ab |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 243 B | — |
| train_stats.json | Configuration | 776 B | — |
| LICENSE | Documentation | 11.3 KB | — |
| README.md | Documentation | 827 B | — |
| chat_template.jinja | Other | 2.5 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 11.4 MB | 3fd169731d2c |
| tokenizer_config.json | Tokenizer | 694 B | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 15.2 GB
Released by Joshua Fonseca through its official repository on Hugging Face.
Built From
- Derived from Qwen/Qwen2.5-7B-Instruct
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 15.2 GB |
| 16-bit | 15.2 GB |
| 8-bit | 7.6 GB |
| 4-bit | 3.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About qwen2.5-7b-fve-advanchor-s0
How much GPU memory does qwen2.5-7b-fve-advanchor-s0 need?
About 18.3 GB at 16-bit and 4.6 GB at 4-bit: the weights (7.6B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run qwen2.5-7b-fve-advanchor-s0 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 qwen2.5-7b-fve-advanchor-s0 released under?
other, as its publisher declares it. Read the license text before commercial use.
What is qwen2.5-7b-fve-advanchor-s0's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.