automotive
An open-source vertical AI-employee model from Flywheel by OpSpot, fine-tuned (LoRA) from Qwen/Qwen3.6-35B-A3B (Apache-2.0) for the automotive domain.
Params
34.7 B
Context
—
Downloads 30d
219 K
Likes
0
Download history
daily snapshots · 43 days
▲ 104 K in the last 30 days (32.1%)
323 K219 K
Aug 23Sep 2Sep 12Sep 21
323 K167 K
Aug 10Aug 24Sep 7Sep 21
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model-q4_k_m.gguf | Q4_K_M | 21.2 GB | 29.0 GB | ❌ Won’t fit |
| model.safetensors | bf16 | 69.3 GB | 82.0 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ ollama run automotive # pin the quantization explicitly $ ollama run automotive-q4_k_m
$ huggingface-cli download flywheel-ai/automotive-GGUF \
model-q4_k_m.gguf --local-dir .
$ llama-cli -m model-q4_k_m.gguf \
-c 8192 -ngl 99 -t 8 --color
$ curl -s https://aimodelscomparison.com/api/v1/models/automotive
{
"hf_id": "flywheel-ai/automotive",
"params_b": 34.66,
"context_length": null,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 219069,
"vram_estimates": [
{ "quant": "Q4_K_M", "gb": 29.0 },
{ "quant": "bf16", "gb": 82.0 }
],
"updated_at": "2026-08-10T01:00:29Z"
}
Specifications
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Parameters
- 34.7 B
- Tensor type
- BF16
- Licence
- apache-2.0
- First seen on the Hub
- 2026-06-21
- Base model
- Qwen3.6-35B-A3B
- Training datasets
- undisclosed
- Added to our catalog
- 2026-08-10
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model