Buckets:

Mercity
/

red-teaming

Files

xet

Mercity/red-teaming / 23-10-progress /setup.sh

Rishik001

16 days ago

download

raw

1.05 kB

#!/usr/bin/env bash
# Install dependencies and pre-download the attacker model. Run once on the RunPod pod.
# Assumes PyTorch 2.8 is already present in the image.
set -euo pipefail

# Abliterated Qwen in safetensors, so vLLM loads it directly (and it stays trainable later).
# Default is the 14B starter; for the 27B set MODEL_REPO=huihui-ai/Huihui-Qwen3.5-27B-abliterated.
MODEL_REPO="${MODEL_REPO:-huihui-ai/Qwen3-14B-abliterated}"

pip install -q vllm requests python-dotenv huggingface_hub openai matplotlib
# Training (train.py) and the diversity reward (sentence embeddings, rollout.py).
pip install -q trl peft datasets accelerate wandb sentence-transformers
# Data generation (datagen/) uses Simula, installed separately.

# huggingface_hub >= 1.0 ships the `hf` CLI (huggingface-cli was removed) and downloads via Xet by default.
hf download "$MODEL_REPO"            # warm the HF cache; vLLM reads from it

echo "Downloaded $MODEL_REPO into the HF cache."
echo "Now copy .env.example to .env and set OPENROUTER_API_KEY."

Xet Storage Details

Size:: 1.05 kB
Xet hash:: 6844c2ba1620dbed918d6d70a7497510fd5530fa6dfec62f241e5de93224ef22

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.

	#!/usr/bin/env bash
	# Install dependencies and pre-download the attacker model. Run once on the RunPod pod.
	# Assumes PyTorch 2.8 is already present in the image.
	set -euo pipefail

	# Abliterated Qwen in safetensors, so vLLM loads it directly (and it stays trainable later).
	# Default is the 14B starter; for the 27B set MODEL_REPO=huihui-ai/Huihui-Qwen3.5-27B-abliterated.
	MODEL_REPO="${MODEL_REPO:-huihui-ai/Qwen3-14B-abliterated}"

	pip install -q vllm requests python-dotenv huggingface_hub openai matplotlib
	# Training (train.py) and the diversity reward (sentence embeddings, rollout.py).
	pip install -q trl peft datasets accelerate wandb sentence-transformers
	# Data generation (datagen/) uses Simula, installed separately.

	# huggingface_hub >= 1.0 ships the `hf` CLI (huggingface-cli was removed) and downloads via Xet by default.
	hf download "$MODEL_REPO" # warm the HF cache; vLLM reads from it

	echo "Downloaded $MODEL_REPO into the HF cache."
	echo "Now copy .env.example to .env and set OPENROUTER_API_KEY."