Instructions to use chaitalibh/mix_grpo_08119_more with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chaitalibh/mix_grpo_08119_more with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("CELL-LAB/lora-plus-f2f-backup") model = PeftModel.from_pretrained(base_model, "chaitalibh/mix_grpo_08119_more") - Notebooks
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- mix_grpo_08119_more
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mix_grpo_08119_more
Final PEFT LoRA adapter from C_SERVER_GRPO_MIXED_EMPTYCTX30_BS8GA3.
Base model: CELL-LAB/lora-plus-f2f-backup
This is the second mixed empty-context GRPO run with a larger no-context mix than the earlier 08118 upload. Training used 289 rows and kept 89 empty RAG-context rows in the training data.
Files
adapter_model.safetensorsadapter_config.json- tokenizer files at repo root and under
tokenizer/ - processor files at repo root and under
processor/ - run/training metadata JSON files
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from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
adapter_id = chaitalibh/mix_grpo_08119_more
base_id = CELL-LAB/lora-plus-f2f-backup
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(base_id, device_map=auto)
model = PeftModel.from_pretrained(base, adapter_id)
Original Adapter Card
base_model: CELL-LAB/lora-plus-f2f-backup library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:CELL-LAB/lora-plus-f2f-backup - grpo - lora - transformers - trl
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Framework versions
- PEFT 0.19.1
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Model tree for chaitalibh/mix_grpo_08119_more
Base model
CELL-LAB/lora-plus-f2f-backup