Instructions to use brainworkup/Qwythos-9B-v2-MLX-oQ8-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/Qwythos-9B-v2-MLX-oQ8-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwythos-9B-v2-MLX-oQ8-mtp brainworkup/Qwythos-9B-v2-MLX-oQ8-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwythos-9B-v2-MLX-oQ8-mtp
This model was quantized using oQ (oMLX v0.5.4.dev1) mixed-precision quantization.
Quantization details
- Model type: qwen3_5
- Bits: 8
- Group size: 64
- Format: MLX safetensors
Testing
I used these params recently for a complex reasoning task requring RAG and high-level thinking. Results were slow but exceptionally strong.
Added kwargs forced reasoning effort = Max
ACTIVE MODEL
Qwythos-9B-v2-MLX-oQ8-mtp
TEMPERATURE
0.6
MAX TOKENS
16384
MIN P
0.95 TOP K 20
REP. PENALTY
1.05
PRESENCE PENALTY
Default
THINKING
On (Unlimited)
- Downloads last month
- 309
Model size
3B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
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Model tree for brainworkup/Qwythos-9B-v2-MLX-oQ8-mtp
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B Finetuned
empero-ai/Qwythos-9B-Claude-Mythos-5-1M Finetuned
empero-ai/Qwythos-9B-v2