Instructions to use SkillScreen/SkillScreen-2B-SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-SD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SD")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SD") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter/adapter_config.json from SkillScreen/SkillScreen-2B-SD: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/adapter/adapter_config.json
- Command line
-
hf download hf://SkillScreen/SkillScreen-2B-SD/adapter/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/adapter/adapter_config.json
1.21 kB
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "AlexWortega/openjev", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "kasa_config": null, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 16, | |
| "lora_bias": false, | |
| "lora_dropout": 0.0, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": [ | |
| "score", | |
| "classifier", | |
| "score" | |
| ], | |
| "monteclora_config": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.21.2", | |
| "qalora_group_size": 16, | |
| "r": 8, | |
| "rank_pattern": {}, | |
| "revision": "a20448012c213128955ca0c693e7c943865cab77", | |
| "target_modules": [ | |
| "v_proj", | |
| "in_proj_qkv", | |
| "q_proj", | |
| "out_proj" | |
| ], | |
| "target_parameters": null, | |
| "task_type": "SEQ_CLS", | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false, | |
| "velora_config": null | |
| } | |