Uncensored version of Qwen/Qwen3.6-35B-A3B with refusal behavior removed via abliteration (norm-preserving orthogonalization). Zero refusals on harmful prompts. No false refusals on harmless prompts. Abliteration identifies the "refusal direction" in the model's residual stream — the linear direction that activates when the model decides to refuse — and surgically removes it from all output projection weights using norm-preserving orthogonalization. 1. Collect residual stream activations (last token position) for 512 harmful + 512 harmless prompts across all 40 layers 2. Compute mean difference vector per layer → this is the "refusal direction" candidate 3. Score layers by…
Anansi-35B-A3B is an open-weight model for text generation from Matthew Andrews, released under Apache License 2.0. It has 34.7B parameters and a 262,144-token context. At 16-bit it needs about 83.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 821 downloads a month.
Anansi-35B-A3B balances good instruct following, narrative reasoning, and sweet prose. Specifically for my 8gb potato, because I can't run the ~30b dense models, but maybe it can be good for your potato too?
Parameters34.7B
Context262,144
Weights69.3 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads821
Runs On
What it takes to serve Anansi-35B-A3B (34.7B 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 | 69.3 GB | 83.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x MI325X $2.00 · 1x MI355X $2.59 |
| 8-bit | 34.7 GB | 41.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 17.3 GB | 20.8 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 16, 2026.
Anansi-35B-A3B on every accelerator the SAVRN Index prices, at every precision
Model Card
By Matthew Andrews, published under apache-2.0, revision e346076f363c.
Anansi-35B-A3B balances good instruct following, narrative reasoning, and sweet prose. Specifically for my 8gb potato, because I can't run the ~30b dense models, but maybe it can be good for your potato too?
Anansi was created by combining the logical trengths of two great models via a 35/65 DARE-TIES merge, followed by a targeted (LM) head tensor interpolation to graft on better prose.
Q8 Prose/Style Head GGUFS with also interpolated Dark Scarlett Prose Head Variants
Quick impression
Configuration
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context length (tokens)
- 262,144
- Layers
- 40
- Hidden size
- 2,048
- Attention heads
- 16
- Key/value heads
- 2
- Head dimension
- 256
- Vocabulary size
- 248,320
- Experts
- 256
- Experts active per token
- 8
- Model type
- qwen3_5_moe
Identity and Version
- Repository
- BlueNipples/Anansi-35B-A3B
- Publisher
- Matthew Andrews
- Task
- Text generation
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 34.7B parameters
- Languages
- Not stated by the source
- Revision
- e346076f363c866d1a2a6f4a4a62fd0eca6303dd
- First published
- 2026-09-19
- Last updated
- 2026-09-22
Files and Weights
25 files, 69.3 GB in total. The weights are 16 files totalling 69.3 GB in safetensors.
Weights16 files · 69.3 GB
Configuration3 files · 73.2 KB
Tokenizer3 files · 16.2 MB
Documentation1 file · 3.2 KB
Other1 file · 2.3 MB
Repository1 file · 1.7 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00016.safetensors | Weights | 4.3 GB | 9ce99f12d8dd |
| model-00002-of-00016.safetensors | Weights | 4.5 GB | c3c118c0f00c |
| model-00003-of-00016.safetensors | Weights | 5.0 GB | 2364a367cba7 |
| model-00004-of-00016.safetensors | Weights | 4.0 GB | 52e2af36d548 |
| model-00005-of-00016.safetensors | Weights | 4.5 GB | 81f1e190fb03 |
| model-00006-of-00016.safetensors | Weights | 5.0 GB | 78b65640b99a |
| model-00007-of-00016.safetensors | Weights | 4.0 GB | 5ba32a0b222e |
| model-00008-of-00016.safetensors | Weights | 5.0 GB | b9a303d120d1 |
| model-00009-of-00016.safetensors | Weights | 4.5 GB | ffe9aa00e3ad |
| model-00010-of-00016.safetensors | Weights | 5.0 GB | 482002ab329a |
| model-00011-of-00016.safetensors | Weights | 4.0 GB | 7dac01f1de08 |
| model-00012-of-00016.safetensors | Weights | 4.5 GB | 4e4f2e8d3bf4 |
| model-00013-of-00016.safetensors | Weights | 5.0 GB | 8c98e99d94c6 |
| model-00014-of-00016.safetensors | Weights | 4.0 GB | c899a9874897 |
| model-00015-of-00016.safetensors | Weights | 4.5 GB | d7326240cccf |
| model-00016-of-00016.safetensors | Weights | 1.6 GB | 4ec3c41e1b1e |
| config.json | Configuration | 3.2 KB | — |
| mergekit_config.yml | Configuration | 345 B | — |
| model.safetensors.index.json | Configuration | 69.7 KB | — |
| README.md | Documentation | 3.2 KB | — |
| Anansi_Image.png | Other | 2.3 MB | a282592ae7a8 |
| .gitattributes | Repository | 1.7 KB | — |
| merges.txt | Tokenizer | 3.4 MB | — |
| tokenizer.json | Tokenizer | 12.8 MB | 5f9e4d4901a9 |
| tokenizer_config.json | Tokenizer | 16.7 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 69.3 GB
Download from Matthew Andrews
Released by Matthew Andrews through its official repository on Hugging Face. Read the license.
Built From
- Derived from Gryphe/WorldSim-Opus-3.6-35B-A3B
- Derived from ReadyArt/Melody1437-35B-A3B
- Derived from huihui-ai/Huihui-Agents-A1-abliterated
- Merged from Gryphe/WorldSim-Opus-3.6-35B-A3B
- Merged from ReadyArt/Melody1437-35B-A3B
- Merged from huihui-ai/Huihui-Agents-A1-abliterated
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 69.3 GB |
| 16-bit | 69.3 GB |
| 8-bit | 34.7 GB |
| 4-bit | 17.3 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Quantized fromAnansi-35B-A3B-Q8-Prose-GGUF
- Derived fromAnansi-35B-A3B-Q8-Prose-GGUF
Questions About Anansi-35B-A3B
How much GPU memory does Anansi-35B-A3B need?
About 83.2 GB at 16-bit and 20.8 GB at 4-bit: the weights (34.7B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Anansi-35B-A3B 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.
Can I use Anansi-35B-A3B commercially?
Yes. Anansi-35B-A3B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Anansi-35B-A3B's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.
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The abliterated version of Longleaf, our local model for coding, repository edits, debugging, and structured tools. - 35B total / 3B active parameters. - MLX 4-bit weights. - 262,144-token configured context. EvalPlus 0.3.1, 164 tasks, pass@1. Thinking off, greedy sampling, 2,048-token output cap, 262,144-token configured context. Generation used our M1 Ultra 231 / 198 / 195 tokens, respectively. CLI sample: five Terminal-Bench 2 tasks on ARM64, thinking off. Per task: 300 seconds, 24 model calls, 32 tool calls, 2,048 output tokens per call. Apache 2.0. This release contains modified model weights, based on Qwen3.6-35B-A3B, and the Abliteration changes refusal behavior, not output…
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