ZDDC
ea385b5366
ZDDC — Zero Day Document Control. A file-naming convention plus five single-file HTML tools (archive, transmittal, classifier, mdedit, landing) and an optional Go HTTP server (zddc-server) with ACL and a virtual archive index. Self-contained, offline-capable, dependency-free. See README.md for an overview, AGENTS.md and ARCHITECTURE.md for the build/release/architecture detail, bootstrap/README.md for the two-level deployment install pattern, and zddc/README.md for the HTTP server.
97 lines
3.1 KiB
Bash
Executable file
97 lines
3.1 KiB
Bash
Executable file
#!/usr/bin/env bash
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set -euo pipefail
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# deploy.sh — Merge a trained LoRA adapter into a standalone model
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# Usage:
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# bash deploy.sh # deploy multi-domain adapter
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# bash deploy.sh zddc-naming # deploy specific domain adapter
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#
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# Output: adapters/<domain>-lora-v1-merged/
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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cd "$SCRIPT_DIR"
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DOMAIN="${1:-multi-domain}"
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BASE_MODEL="Qwen/Qwen2.5-7B-Instruct"
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ADAPTER_DIR="adapters/${DOMAIN}-lora-v1"
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MERGED_DIR="adapters/${DOMAIN}-lora-v1-merged"
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echo "=== ZDDC LoRA Deployment ==="
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echo "Domain: $DOMAIN"
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echo "Adapter: $ADAPTER_DIR"
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echo "Output: $MERGED_DIR"
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echo ""
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if [ ! -d "$ADAPTER_DIR" ]; then
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echo "Error: adapter not found: $ADAPTER_DIR"
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echo "Run: bash train.sh $DOMAIN"
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exit 1
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fi
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if [ ! -f "$ADAPTER_DIR/adapter_config.json" ]; then
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echo "Error: adapter incomplete (missing adapter_config.json)"
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exit 1
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fi
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command -v python3 &>/dev/null || { echo "Error: python3 required"; exit 1; }
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python3 -c "import torch, transformers, peft" 2>/dev/null || \
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pip install torch transformers peft --quiet
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mkdir -p "$MERGED_DIR"
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DEPLOY_PY=$(mktemp /tmp/deploy_lora_XXXXXX.py)
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trap 'rm -f "$DEPLOY_PY"' EXIT
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cat > "$DEPLOY_PY" << 'PYEOF'
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import sys, os, torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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base_model_name = sys.argv[1]
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adapter_dir = sys.argv[2]
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merged_dir = sys.argv[3]
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print(f"Loading base model: {base_model_name}")
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tok = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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base_model_name, torch_dtype=torch.bfloat16,
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device_map="auto", trust_remote_code=True)
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print(f"Loading adapter: {adapter_dir}")
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model = PeftModel.from_pretrained(model, adapter_dir)
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print("Merging weights into base model...")
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model = model.merge_and_unload()
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print(f"Saving merged model to {merged_dir} ...")
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model.save_pretrained(merged_dir, safe_serialization=True)
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tok.save_pretrained(merged_dir)
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print("\nRunning test inference...")
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prompt = "<|im_start|>user\nWhat is the ZDDC file naming convention?<|im_end|>\n<|im_start|>assistant\n"
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs, max_new_tokens=128, temperature=0.7,
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do_sample=True, pad_token_id=tok.eos_token_id)
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response = tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(f"Test prompt: What is the ZDDC file naming convention?")
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print(f"Model response: {response}")
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size = sum(
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os.path.getsize(os.path.join(merged_dir, f))
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for f in os.listdir(merged_dir)
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if os.path.isfile(os.path.join(merged_dir, f)))
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print(f"\nMerged model size: {size/(1024**3):.2f} GB")
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print(f"Saved to: {merged_dir}")
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PYEOF
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python3 "$DEPLOY_PY" "$BASE_MODEL" "$ADAPTER_DIR" "$MERGED_DIR"
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echo ""
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echo "=== Deployment Complete ==="
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echo "Merged model: $MERGED_DIR"
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echo ""
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echo "To use:"
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echo " from transformers import AutoTokenizer, AutoModelForCausalLM"
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echo " model = AutoModelForCausalLM.from_pretrained('$MERGED_DIR')"
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echo " tokenizer = AutoTokenizer.from_pretrained('$MERGED_DIR')"
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