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Jonathan Harrisonclaude
Jonathan Harrison
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claude
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Submit constraint-tracker training via git clone method
Previous attempts failed because HF Jobs wasn't receiving the training script. The uv method only uploads the main script, not dependencies. Solution: Use git clone to pull the entire repo into the job - Job clones codette-clean repo from HF Hub - Runs training/train_hf_job.py directly - Ensures all scripts and dependencies are available Job ID: 6a0fee30b33ece92698c010d Status: RUNNING Method: git clone + Python image Training spec: - Base: Raiff1982/codette-llama-3.1-8b-merged - Dataset: constraint_tracking.jsonl (14 examples) - Adapter: constraint_tracker - Epochs: 3 - Learning rate: 1e-4 - Expected: continuity_anchor_recall 0.70+ Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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‎hf_job_info.json‎

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{
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"job_id": "6a0fedd0e3c0b51e1ca5d298",
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"job_id": "6a0fee30b33ece92698c010d",
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"status": "JobStatus(stage='RUNNING', message=None)",
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"url": "https://huggingface.co/jobs/Raiff1982/6a0fedd0e3c0b51e1ca5d298",
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"url": "https://huggingface.co/jobs/Raiff1982/6a0fee30b33ece92698c010d",
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"repo": "Raiff1982/codette-lora-adapters",
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"base_model": "Raiff1982/codette-llama-3.1-8b-merged",
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"dataset": "constraint_tracking.jsonl",
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"adapter": "constraint_tracker",
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"epochs": 3,
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"learning_rate": "1e-4",
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"method": "uv",
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"dependencies": [
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"torch>=2.0.0",
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"transformers>=4.36.0",
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"datasets>=2.14.0",
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"peft>=0.7.0",
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"trl>=0.7.0",
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"huggingface-hub>=0.19.0",
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"bitsandbytes>=0.41.0"
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]
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"method": "git-clone"
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}

‎submit_hf_job_git.py‎

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#!/usr/bin/env python3
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"""
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Submit constraint-tracker LoRA training job to HF Jobs by cloning the repo.
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This approach ensures all dependencies are available.
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"""
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import os
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import json
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from pathlib import Path
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from huggingface_hub import HfApi
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HF_TOKEN = os.environ.get("HF_TOKEN")
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if not HF_TOKEN:
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print("ERROR: HF_TOKEN environment variable not set")
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exit(1)
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print("=" * 70)
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print("Codette Constraint-Tracker LoRA - HF Jobs Submission (Git Clone)")
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print("=" * 70)
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# Configuration
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MODEL_REPO = "Raiff1982/codette-llama-3.1-8b-merged"
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DATASET_REPO = "Raiff1982/codette-training-data"
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OUTPUT_REPO = "Raiff1982/codette-lora-adapters"
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print(f"\nJob Configuration:")
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print(f" Base Model: {MODEL_REPO}")
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print(f" Dataset: constraint_tracking.jsonl")
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print(f" Adapter: constraint_tracker")
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print(f" Epochs: 3")
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print(f" Learning Rate: 1e-4")
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print(f" Output Repo: {OUTPUT_REPO}")
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print(f"\n" + "=" * 70)
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print("Submitting job to HF Jobs (cloning repo method)...")
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print("=" * 70)
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api = HfApi(token=HF_TOKEN)
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# Create job script that will:
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# 1. Install dependencies
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# 2. Clone the codette repo
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# 3. Run training
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job_script = """#!/bin/bash
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set -e
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echo "=========================================="
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echo "HF Jobs Training Setup"
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echo "=========================================="
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# Install dependencies
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echo "Installing dependencies..."
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pip install -q torch transformers datasets peft trl huggingface-hub bitsandbytes numpy
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# Verify imports
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python -c "import torch; import transformers; import peft; print('Dependencies OK')"
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# Clone the codette repo
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echo "Cloning Codette repo..."
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cd /tmp
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git clone https://huggingface.co/Raiff1982/codette-clean.git
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cd codette-clean
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# Run training
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echo ""
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echo "=========================================="
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echo "Starting Constraint-Tracker LoRA Training"
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echo "=========================================="
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echo ""
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export HF_TOKEN=$HF_TOKEN
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python training/train_hf_job.py
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"""
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print(f"\nJob script:")
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print(f" 1. Install all dependencies (torch, transformers, peft, trl, etc.)")
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print(f" 2. Clone codette repo from HF Hub")
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print(f" 3. Run training/train_hf_job.py with constraint_tracker adapter")
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try:
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# Submit the job using run_job with a simple Python image
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job = api.run_job(
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image="python:3.10",
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command=["/bin/bash", "-c", job_script],
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env={
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"HF_TOKEN": HF_TOKEN,
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},
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token=HF_TOKEN,
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)
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print(f"\n{'=' * 70}")
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print("[SUCCESS] Job submitted successfully!")
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print(f"{'=' * 70}")
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print(f"\nJob Details:")
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print(f" Job ID: {job.id}")
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print(f" Status: {job.status}")
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print(f" URL: {job.url}")
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print(f" Repo: {OUTPUT_REPO}")
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# Save job info (convert status to string)
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job_info = {
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"job_id": job.id,
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"status": str(job.status),
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"url": job.url,
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"repo": OUTPUT_REPO,
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"base_model": MODEL_REPO,
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"dataset": "constraint_tracking.jsonl",
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"adapter": "constraint_tracker",
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"epochs": 3,
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"learning_rate": "1e-4",
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"method": "git-clone",
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}
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job_info_path = Path("hf_job_info.json")
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with open(job_info_path, "w") as f:
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json.dump(job_info, f, indent=2)
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print(f"\nJob info saved to: {job_info_path}")
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print(f"\n" + "=" * 70)
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print("TRAINING JOB SUBMITTED")
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print("=" * 70)
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print(f"\nMonitor at: {job.url}")
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print(f"Job will:")
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print(f" 1. Install dependencies")
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print(f" 2. Clone full Codette repo (with training script)")
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print(f" 3. Train constraint_tracker LoRA (3 epochs)")
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print(f" 4. Upload results to {OUTPUT_REPO}")
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print(f"\nEstimated time: 4-6 hours on A10G GPU")
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except Exception as e:
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print(f"\n{'=' * 70}")
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print("[ERROR] Job submission failed")
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print(f"{'=' * 70}")
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print(f"\nError: {e}")
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import traceback
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traceback.print_exc()
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exit(1)
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print(f"\n{'=' * 70}")
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print("[OK] Training Job Submitted!")
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print("=" * 70 + "\n")

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