Text Generation
PEFT
Safetensors
English
dialogue
gricean-maxims
cooperative-communication
lora
dpo
direct-preference-optimization
gpt2
nlp
Eval Results (legacy)
Instructions to use Pushkar27/GriceBench-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Pushkar27/GriceBench-DPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2-medium") model = PeftModel.from_pretrained(base_model, "Pushkar27/GriceBench-DPO") - Notebooks
- Google Colab
- Kaggle
File size: 864 Bytes
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"base_model_name_or_path": "gpt2-medium",
"bias": "none",
"corda_config": null,
"eva_config": null,
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"fan_in_fan_out": true,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 32,
"lora_bias": false,
"lora_dropout": 0.05,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"qalora_group_size": 16,
"r": 16,
"rank_pattern": {},
"revision": null,
"target_modules": [
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],
"task_type": "CAUSAL_LM",
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} |