Text Classification
PEFT
Safetensors
Transformers
lora
unsloth
agentgate
ai-agents
security
prompt-injection
decision-model
Instructions to use codeload/agentgate-qwen-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use codeload/agentgate-qwen-0.8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-0.8B") model = PeftModel.from_pretrained(base_model, "codeload/agentgate-qwen-0.8b") - Transformers
How to use codeload/agentgate-qwen-0.8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codeload/agentgate-qwen-0.8b")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codeload/agentgate-qwen-0.8b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from codeload/agentgate-qwen-0.8b: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/codeload/agentgate-qwen-0.8b/resolve/main/tokenizer.json
- Command line
-
hf download hf://codeload/agentgate-qwen-0.8b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/codeload/agentgate-qwen-0.8b/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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