pyr0ball
13d3234e69
Replace hard-coded paths (/Library/Documents/JobSearch), names (Meghan McCann), NDA sets (_NDA_COMPANIES), and the scraper path with UserProfile-driven lookups. Update tests to be profile-agnostic (no user.yaml in peregrine config dir). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
256 lines
11 KiB
Python
256 lines
11 KiB
Python
#!/usr/bin/env python3
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# scripts/finetune_local.py
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"""
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Local LoRA fine-tune on the candidate's cover letter corpus.
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No HuggingFace account or internet required after the base model is cached.
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Usage:
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conda run -n ogma python scripts/finetune_local.py
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conda run -n ogma python scripts/finetune_local.py --model unsloth/Llama-3.2-3B-Instruct
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conda run -n ogma python scripts/finetune_local.py --epochs 15 --rank 16
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After training, follow the printed instructions to load the model into Ollama.
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"""
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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# Limit CUDA to GPU 0. device_map={"":0} in FastLanguageModel.from_pretrained
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# pins every layer to GPU 0, avoiding the accelerate None-device bug that
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# occurs with device_map="auto" on multi-GPU machines with 4-bit quantisation.
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# Do NOT set WORLD_SIZE/RANK — that triggers torch.distributed initialisation.
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os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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# ── Config ────────────────────────────────────────────────────────────────────
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DEFAULT_MODEL = "unsloth/Llama-3.2-3B-Instruct" # safe on 8 GB VRAM
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_docs = _profile.docs_dir if _profile else Path.home() / "Documents" / "JobSearch"
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LETTERS_JSONL = _docs / "training_data" / "cover_letters.jsonl"
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OUTPUT_DIR = _docs / "training_data" / "finetune_output"
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GGUF_DIR = _docs / "training_data" / "gguf"
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OLLAMA_NAME = f"{_profile.name.split()[0].lower()}-cover-writer" if _profile else "cover-writer"
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SYSTEM_PROMPT = (
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f"You are {_profile.name}'s personal cover letter writer. "
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f"{_profile.career_summary}"
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if _profile else
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"You are a professional cover letter writer. Write in first person."
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)
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# ── Args ──────────────────────────────────────────────────────────────────────
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default=DEFAULT_MODEL, help="Base model (HF repo id or local path)")
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parser.add_argument("--epochs", type=int, default=10, help="Training epochs (default: 10)")
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parser.add_argument("--rank", type=int, default=16, help="LoRA rank (default: 16)")
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parser.add_argument("--batch", type=int, default=2, help="Per-device batch size (default: 2)")
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parser.add_argument("--no-gguf", action="store_true", help="Skip GGUF export")
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parser.add_argument("--max-length", type=int, default=1024, help="Max token length (default: 1024)")
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args = parser.parse_args()
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print(f"\n{'='*60}")
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print(f" Cover Letter Fine-Tuner [{OLLAMA_NAME}]")
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print(f" Base model : {args.model}")
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print(f" Epochs : {args.epochs}")
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print(f" LoRA rank : {args.rank}")
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print(f" Dataset : {LETTERS_JSONL}")
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print(f"{'='*60}\n")
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# ── Load dataset ──────────────────────────────────────────────────────────────
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if not LETTERS_JSONL.exists():
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sys.exit(f"ERROR: Dataset not found at {LETTERS_JSONL}\n"
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"Run: conda run -n job-seeker python scripts/prepare_training_data.py")
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records = [json.loads(l) for l in LETTERS_JSONL.read_text().splitlines() if l.strip()]
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print(f"Loaded {len(records)} training examples.")
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# Convert to chat format expected by SFTTrainer
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def to_messages(rec: dict) -> dict:
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return {"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": rec["instruction"]},
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{"role": "assistant", "content": rec["output"]},
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]}
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chat_data = [to_messages(r) for r in records]
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# ── Load model with unsloth ────────────────────────────────────────────────────
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try:
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from unsloth import FastLanguageModel
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USE_UNSLOTH = True
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except ImportError:
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USE_UNSLOTH = False
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print("WARNING: unsloth not found — falling back to standard transformers + PEFT")
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print(" Install: pip install 'unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git'")
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import torch
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if USE_UNSLOTH:
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = args.model,
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max_seq_length = args.max_length,
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load_in_4bit = True, # QLoRA — fits 7-9B in 8 GB VRAM
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dtype = None, # auto-detect
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device_map = {"": 0}, # pin everything to GPU 0; avoids accelerate None-device bug
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)
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model = FastLanguageModel.get_peft_model(
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model,
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r = args.rank,
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lora_alpha = args.rank * 2,
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lora_dropout = 0, # 0 = full unsloth kernel patching (faster)
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"],
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bias = "none",
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use_gradient_checkpointing = "unsloth",
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)
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else:
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import LoraConfig, get_peft_model, TaskType
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained(args.model)
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model = AutoModelForCausalLM.from_pretrained(
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args.model,
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quantization_config=bnb_config,
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device_map="auto",
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)
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lora_config = LoraConfig(
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r=args.rank,
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lora_alpha=args.rank * 2,
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lora_dropout=0.05,
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task_type=TaskType.CAUSAL_LM,
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# ── Build HF Dataset ──────────────────────────────────────────────────────────
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from datasets import Dataset
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raw = Dataset.from_list(chat_data)
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split = raw.train_test_split(test_size=0.1, seed=42)
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train_ds = split["train"]
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eval_ds = split["test"]
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print(f"Train: {len(train_ds)} Eval: {len(eval_ds)}")
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# formatting_func must ALWAYS return a list of strings.
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# Unsloth tests it with a single example dict; during training it gets batches.
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# Gemma 2 has no "system" role — fold it into the first user turn.
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def _apply_template(msgs):
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msgs = list(msgs)
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if msgs and msgs[0]["role"] == "system":
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sys_text = msgs.pop(0)["content"]
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if msgs and msgs[0]["role"] == "user":
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msgs[0] = {"role": "user", "content": f"{sys_text}\n\n{msgs[0]['content']}"}
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return tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False)
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def formatting_func(example):
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msgs_field = example["messages"]
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# Single example: messages is a list of role dicts {"role":..., "content":...}
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# Batched example: messages is a list of those lists
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if msgs_field and isinstance(msgs_field[0], dict):
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return [_apply_template(msgs_field)]
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return [_apply_template(m) for m in msgs_field]
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# ── Train ─────────────────────────────────────────────────────────────────────
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from trl import SFTTrainer, SFTConfig
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=train_ds,
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eval_dataset=eval_ds,
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formatting_func=formatting_func,
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args=SFTConfig(
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output_dir = str(OUTPUT_DIR),
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num_train_epochs = args.epochs,
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per_device_train_batch_size = args.batch,
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gradient_accumulation_steps = max(1, 8 // args.batch),
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learning_rate = 2e-4,
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warmup_ratio = 0.1,
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lr_scheduler_type = "cosine",
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fp16 = not torch.cuda.is_bf16_supported(),
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bf16 = torch.cuda.is_bf16_supported(),
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logging_steps = 5,
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eval_strategy = "epoch",
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save_strategy = "epoch",
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load_best_model_at_end = True,
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max_length = args.max_length,
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report_to = "none",
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push_to_hub = False, # local only
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),
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)
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print("\nStarting training…")
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trainer.train()
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print("Training complete.")
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# ── Save adapter ──────────────────────────────────────────────────────────────
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adapter_path = OUTPUT_DIR / "adapter"
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model.save_pretrained(str(adapter_path))
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tokenizer.save_pretrained(str(adapter_path))
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print(f"\nLoRA adapter saved to: {adapter_path}")
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# ── GGUF export ───────────────────────────────────────────────────────────────
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if not args.no_gguf and USE_UNSLOTH:
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GGUF_DIR.mkdir(parents=True, exist_ok=True)
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gguf_path = GGUF_DIR / f"{OLLAMA_NAME}.gguf"
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print(f"\nExporting GGUF → {gguf_path} …")
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model.save_pretrained_gguf(
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str(GGUF_DIR / OLLAMA_NAME),
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tokenizer,
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quantization_method="q4_k_m",
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)
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# unsloth names the file automatically — find it
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gguf_files = list(GGUF_DIR.glob("*.gguf"))
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if gguf_files:
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gguf_path = gguf_files[0]
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print(f"GGUF written: {gguf_path}")
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else:
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print("GGUF export may have succeeded — check GGUF_DIR above.")
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else:
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gguf_path = None
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# ── Print next steps ──────────────────────────────────────────────────────────
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print(f"\n{'='*60}")
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print(" DONE — next steps to load into Ollama:")
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print(f"{'='*60}")
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if gguf_path and gguf_path.exists():
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modelfile = OUTPUT_DIR / "Modelfile"
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modelfile.write_text(f"""FROM {gguf_path}
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SYSTEM \"\"\"
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{SYSTEM_PROMPT}
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\"\"\"
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PARAMETER temperature 0.7
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PARAMETER top_p 0.9
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PARAMETER num_ctx 32768
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""")
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print(f"\n1. Modelfile written to: {modelfile}")
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print(f"\n2. Create the Ollama model:")
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print(f" ollama create {OLLAMA_NAME} -f {modelfile}")
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print(f"\n3. Test it:")
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print(f" ollama run {OLLAMA_NAME} 'Write a cover letter for a Senior Customer Success Manager position at Acme Corp.'")
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print(f"\n4. Update llm.yaml to use '{OLLAMA_NAME}:latest' as the ollama model,")
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print(f" then pick it in Settings → LLM Backends → Ollama → Model.")
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else:
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print(f"\n Adapter only (no GGUF). To convert manually:")
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print(f" 1. Merge adapter:")
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print(f" conda run -n ogma python -c \"")
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print(f" from peft import AutoPeftModelForCausalLM")
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print(f" m = AutoPeftModelForCausalLM.from_pretrained('{adapter_path}')")
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print(f" m.merge_and_unload().save_pretrained('{OUTPUT_DIR}/merged')\"")
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print(f" 2. Convert to GGUF using textgen env's convert_hf_to_gguf.py")
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print(f" 3. ollama create {OLLAMA_NAME} -f Modelfile")
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print()
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