CellHermes-CoT-RL

CellHermes-CoT-RL

This repository contains the reinforcement-learning LoRA adapter used for the CellHermes-CoT-RL row in the TCR reactivity benchmark.

Important: Adapter-Only Repository

This repository is not a standalone merged model checkpoint. It contains only a PEFT LoRA adapter:

• adapter_config.json
• adapter_model.safetensors

These two files are sufficient for distributing and loading the RL LoRA adapter with PEFT-compatible tooling, but they are not sufficient to run inference by themselves.

To reproduce the benchmark setup, load this adapter on top of the merged CellHermes-CoT-SFT checkpoint. The merged SFT checkpoint provides the base model weights, tokenizer, chat template, and SFT reasoning format; this repository provides only the post-SFT RL policy update.

Base Model for This Adapter

This adapter must be loaded on the merged SFT checkpoint generated from EthanGao123/CellHermes-CoT-SFT and the same CellHermes-v1.0 base model.

It should not be loaded directly on the original CellHermes-v1.0 base model.

Adapter

• Adapter files in this repository: adapter_config.json, adapter_model.safetensors
• Adapter type: LoRA
• LoRA rank: 32
• LoRA alpha: 64
• LoRA dropout: 0.0
• LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Source-data variant: TCR reactivity benchmark, clone split with 25% held-out clone groups, seed 2025
• RL method: GSPO-style policy optimization with structured TCR-reactivity rewards
• Selected RL checkpoint: post-SFT RL checkpoint used for the benchmark
• Reward function family: structured TCR reactivity reward
• Benchmark row: CellHermes-CoT-RL

Inference Alignment

For the plotted benchmark, vLLM inference used:

• model: the merged CellHermes-CoT-SFT checkpoint
• lora: this repository

The SFT merged base provides the tokenizer, chat template, and SFT reasoning format. This RL LoRA adapter provides the post-SFT policy update.

Example PEFT-style loading pattern:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

sft_merged_base = "path_or_repo_to_merged_CellHermes-CoT-SFT"
rl_adapter = "EthanGao123/CellHermes-CoT-RL"

tokenizer = AutoTokenizer.from_pretrained(sft_merged_base)
base_model = AutoModelForCausalLM.from_pretrained(sft_merged_base)
model = PeftModel.from_pretrained(base_model, rl_adapter)

Alignment Notes

• Load this adapter only with the SFT merged base checkpoint listed above.
• Keep tokenizer files and chat_template.jinja from the SFT merged base checkpoint.
• Do not merge or serve this adapter against raw CellHermes-v1.0, raw Meta-Llama, or another SFT checkpoint unless intentionally rerunning a different experiment.
• The benchmark predictions for CellHermes-CoT-RL were generated from the SFT merged base plus this RL LoRA adapter.
• Optimizer states, scheduler states, RNG states, and trainer logs are intentionally not included because they are not required for adapter inference.