Text Classification
Transformers
Safetensors
English
Korean
Japanese
qev_decision_merged
feature-extraction
qev
dlp
typed-decision
japanese
bf16
custom_code
Instructions to use querypieai/QEV2.1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use querypieai/QEV2.1-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="querypieai/QEV2.1-12B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("querypieai/QEV2.1-12B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/quantization-config.example.json from querypieai/QEV2.1-12B: direct link, hf CLI and curl.
- Browser
- Download file 393 Bytes
-
https://huggingface.co/querypieai/QEV2.1-12B/resolve/main/code/quantization-config.example.json
- Command line
-
hf download hf://querypieai/QEV2.1-12B/code/quantization-config.example.json
-
curl -L -o quantization-config.example.json https://huggingface.co/querypieai/QEV2.1-12B/resolve/main/code/quantization-config.example.json
393 Bytes
| { | |
| "recipe": "bitsandbytes LLM.int8", | |
| "load_in_8bit": true, | |
| "llm_int8_threshold": 6.0, | |
| "llm_int8_has_fp16_weight": false, | |
| "llm_int8_skip_modules": ["lm_head"], | |
| "floating_compute_dtype": "bfloat16", | |
| "pointer_head_dtype": "float32", | |
| "adapter_merged": false, | |
| "serialization": "safetensors", | |
| "max_shard_size": "4GB", | |
| "device": "single CUDA GPU", | |
| "automatic_upload": false | |
| } | |