Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: apache-2.0Files
| File | Format |
|---|---|
model-00001-of-00002.safetensors + model-00002-of-00002.safetensors | sharded safetensors |
tokenizer.json, tokenizer_config.json, special_tokens_map.json | tokenizer |
config.json, generation_config.json | model config |
chat_template.jinja | chat template |
Run
Hosted via the Hanzo gateway (api.hanzo.ai) as zen5-flash — see https://docs.hanzo.ai/zen.
Local with the zen5-engine or any transformers-compatible runtime:
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("zenlm/zen-5-flash-gguf")model = AutoModelForCausalLM.from_pretrained("zenlm/zen-5-flash-gguf", device_map="auto")
Acknowledgements
Built on Qwen/Qwen3-4B-Instruct-2507 (Apache-2.0) with refusal-direction-orthogonalized weights to improve agentic dual-use task handling.
Model provider
zenlm
Model tree
Base
Qwen/Qwen3-4B-Instruct-2507
Fine-tuned
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information