Open-weight model
qwen3.5-9b-fve-workanchor-s0
by Joshua Fonseca joshycodes/qwen3.5-9b-fve-workanchor-s0
qwen3.5-9b-fve-workanchor-s0 is an open-weight model from Joshua Fonseca, released under other. It has 9B parameters and a 262,144-token context. At 16-bit it needs about 21.5 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/Qwen3.5-9B was continued-pretrained (full weights, lr 1e-05, 1 epoch, 7,189,825 tokens, 7,740 documents, of which 0 self-authored and 7,740 ordinary text) on a corpus the model wrote for the training of the next version of itself, as…
Runs On
What it takes to serve qwen3.5-9b-fve-workanchor-s0 (9B 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 | 17.9 GB | 21.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 9.0 GB | 10.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 4.5 GB | 5.4 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.
qwen3.5-9b-fve-workanchor-s0 on every accelerator the SAVRN Index prices, at every precision
Model Card
Research checkpoint. Qwen/Qwen3.5-9B was continued-pretrained (full weights, lr 1e-05, 1 epoch, 7,189,825 tokens, 7,740 documents, of which 0 self-authored and 7,740 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
- Qwen3_5ForCausalLM
- Context length (tokens)
- 262,144
- Layers
- 32
- Hidden size
- 4,096
- Feed-forward size
- 12,288
- Attention heads
- 16
- Key/value heads
- 4
- Head dimension
- 256
- Vocabulary size
- 248,320
- Stored precision
- bfloat16
- Model type
- qwen3_5_text
Identity and Version
- Repository
- joshycodes/qwen3.5-9b-fve-workanchor-s0
- Publisher
- Joshua Fonseca
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 9B parameters
- Languages
- Not stated by the source
- Revision
- 655359d6026be800764e5129f885c828f220868c
- First published
- 2026-09-25
- Last updated
- 2026-09-25
Files and Weights
14 files, 17.9 GB in total. The weights are 1 file totalling 17.9 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 17.9 GB | 4ac81e4ef9c4 |
| config.json | Configuration | 2.0 KB | — |
| generation_config.json | Configuration | 116 B | — |
| preprocessor_config.json | Configuration | 390 B | — |
| train_stats.json | Configuration | 834 B | — |
| video_preprocessor_config.json | Configuration | 385 B | — |
| LICENSE | Documentation | 11.5 KB | — |
| README.md | Documentation | 800 B | — |
| chat_template.jinja | Other | 7.8 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 3.4 MB | — |
| tokenizer.json | Tokenizer | 20.0 MB | 06b9509352d2 |
| tokenizer_config.json | Tokenizer | 1.1 KB | — |
| vocab.json | Tokenizer | 6.7 MB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 17.9 GB
Released by Joshua Fonseca through its official repository on Hugging Face.
Built From
- Derived from Qwen/Qwen3.5-9B
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 17.9 GB |
| 16-bit | 17.9 GB |
| 8-bit | 9.0 GB |
| 4-bit | 4.5 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About qwen3.5-9b-fve-workanchor-s0
How much GPU memory does qwen3.5-9b-fve-workanchor-s0 need?
About 21.5 GB at 16-bit and 5.4 GB at 4-bit: the weights (9B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run qwen3.5-9b-fve-workanchor-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 qwen3.5-9b-fve-workanchor-s0 released under?
other, as its publisher declares it. Read the license text before commercial use.
What is qwen3.5-9b-fve-workanchor-s0's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.