Training scripts and DeepSpeed configs

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README.md

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+---
+license: apache-2.0
+tags:
+  - training
+  - reproduction
+  - qwen
+  - deepspeed
+  - consumer-gpu
+  - 4bit-quantization
+---
+
+# GoodGlinda-7B Training Code
+
+[![Model](https://img.shields.io/badge/🤗%20Model-GoodGlinda--7B--Verifier-blue)](https://huggingface.co/YellowLabsStudio/goodglinda-7b-verifier)
+[![Dataset](https://img.shields.io/badge/🤗%20Dataset-Training--Data-blue)](https://huggingface.co/datasets/YellowLabsStudio/goodglinda-training-data)
+[![Space](https://img.shields.io/badge/🤗%20Space-Live--Demo-blue)](https://huggingface.co/spaces/YellowLabsStudio/goodglinda-7b-eval)
+[![Code](https://img.shields.io/badge/🤗%20Code-Training--Scripts-blue)](https://huggingface.co/YellowLabsStudio/goodglinda-training-code)
+![HomeLab AI](https://img.shields.io/badge/Research-HomeLab%20AI-gold)
+![Independent](https://img.shields.io/badge/Status-Independent-black)
+![Hardware](https://img.shields.io/badge/HW-RTX%204060%2F5070Ti-green)
+![License](https://img.shields.io/badge/License-Apache%202.0-blue)
+
+The model runs a hierarchical three-tier architecture on consumer hardware. I trained it on an Intel Core i7-12700 with an RTX 4060 (8GB) and RTX 5070 Ti (16GB) Overclocked and Undervoltaged in an asymmetric configuration that left the 5070 Ti idle 30% of the time. At hour 14, the 4060 hit 83°C and throttled. I replaced the thermal paste at hour 18 and watched temperatures stabilize at 79°C for the remaining 54 hours.
+I recommend using Watercooling or other liquid cooling methods for your easiness
+
+I initially wasted two days trying to implement pipeline parallelism before admitting defeat and switching to DeepSpeed ZeRO-2 with CPU offloading.
+
+## My Hardware Setup
+
+*   **CPU:** Intel Core i7-12700 (12th Gen)
+*   **GPU 0:** RTX 4060 (8GB VRAM) - Auxiliary/Offloading (throttled at 83°C before paste fix)
+*   **GPU 1:** RTX 5070 Ti (16GB VRAM) - Primary training (30% idle time due to asymmetry)
+*   **RAM:** 64GB DDR5-4800
+*   **Storage:** 2TB NVMe Gen4
+*   **Power Supply:** 850W (upgraded from 650W mid-training after voltage drops)
+
+## Quick Start
+
+Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+Generate the dataset (I used DeepSeek-V2 as teacher):
+
+```bash
+python prepare_data.py \
+  --output_dir ./data \
+  --num_samples 50000 \
+  --teacher_model deepseek-ai/deepseek-llm-7b-chat
+```
+
+Train (72 hours, single run):
+
+```bash
+deepspeed --num_gpus=2 train.py \
+  --deepspeed ds_config.json \
+  --model_name Qwen/Qwen2.5-7B-Instruct \
+  --output_dir ./output \
+  --num_train_epochs 3 \
+  --learning_rate 2e-4 \
+  --warmup_steps 500
+```
+
+## Key Configurations
+
+I used 4-bit NormalFloat with double quantization to squeeze into 8GB.
+
+| Parameter | Value | Notes |
+|-----------|--------|-------|
+| Quantization | 4-bit NormalFloat | Double quantization enabled |
+| Optimizer | AdamW 8-bit | CPU offloading via DeepSpeed ZeRO-2 |
+| Effective Batch Size | 8 | 2 per GPU × 2 gradient accumulation |
+| Learning Rate | 2e-4 | Cosine decay with 10% warmup |
+| LoRA Rank | 64 | Targeting q, k, v, o projections |
+| Training Duration | 72 hours | Continuous, single run, no restarts |
+| Peak Temp (4060) | 83°C -> 79°C | After thermal paste replacement |
+
+## Repository Structure
+
+```
+├── train.py                 # Main training script (simplified skeleton)
+├── ds_config.json           # DeepSpeed ZeRO-2 config for asymmetric VRAM
+├── prepare_data.py          # Dataset generation using DeepSeek-V2
+├── verify_data.py           # Validation checks
+├── requirements.txt         # Locked versions
+├── logs/
+│   ├── loss_curves.png      # I screengrabbed this at hour 68
+│   ├── gpu_utilization.log  # Shows the 30% idle time on 5070 Ti
+│   └── thermal_stats.log    # 83°C spike visible at hour 14
+└── checkpoints/             # Saved every 500 steps
+```
+
+
+## What I Learned
+
+**Pipeline Parallelism Failure:** I spent two days trying to split layers across the 8GB and 16GB cards manually. It failed constantly due to communication overhead. ZeRO-2 with CPU offloading solved this in 20 minutes but left the 5070 Ti underutilized.
+
+**Thermal Management:** The RTX 4060 required aggressive intervention. I set a custom fan curve (80% speed at 75°C), replaced the thermal paste at hour 18 (dropped temps by 4°C), and added case fans. Without these, the card would throttle to 2.1GHz, adding roughly 40% to training time.
+Watercooled should have been a better solution.. maybe
+
+**Single Run Limitations:** multiple seeds at home lab was not possible. This is a single 72-hour run. Your results may vary ±3-5% due to random initialization.
+
+## Reproducibility Notes
+
+What you can reproduce:
+*   Training procedure with identical hyperparameters
+*   Dataset generation pipeline (requires DeepSeek-V2 API access)
+*   Verification protocol on 200 samples
+
+Known limitations:
+*   Single training run (no seed averaging)
+*   Exact loss curves may vary ±3-5% due to hardware noise
+*   Thermal throttling events may affect timing (depends on ambient temperature)
+
+Details on the full methodology will appear in an upcoming publication.
+
+## Troubleshooting
+
+**CUDA OOM Errors:** I hit these constantly on the 4060. Reduce per_device_train_batch_size to 1, increase gradient_accumulation_steps to 4, or enable more aggressive gradient checkpointing.
+
+**Thermal Throttling:** Monitor with nvidia-smi dmon. Target <83°C sustained. Consider undervolting (I stabilized at 0.95V @ 2.75GHz).
+
+**Slow Training:** Expected throughput is ~1.8 samples/sec with my asymmetric setup. A symmetric dual-16GB setup would hit ~2.5 samples/sec, but I cannot afford that. The PCIe bottleneck is the limiting factor, not compute.
+
+## License
+
+Apache 2.0. Commercial use permitted with attribution.

ds_config.json

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+{
+    "bf16": {
+      "enabled": true
+    },
+    "zero_optimization": {
+      "stage": 2,
+      "offload_optimizer": {
+        "device": "cpu",
+        "pin_memory": true
+      },
+      "allgather_partitions": true,
+      "allgather_bucket_size": 2e8,
+      "overlap_comm": true,
+      "reduce_scatter": true,
+      "reduce_bucket_size": 2e8,
+      "contiguous_gradients": true
+    },
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": 1.0,
+    "wall_clock_breakdown": false
+  }

requirements.txt

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+torch==2.2.0
+transformers==4.38.0
+deepspeed==0.13.0
+peft==0.9.0
+bitsandbytes==0.42.0
+accelerate==0.27.0
+datasets==2.16.0

train.py

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+#!/usr/bin/env python3
+"""
+Training script for GoodGlinda-7B
+Simplified reproduction skeleton - I ran this for 72 hours straight on my i7-12700 + RTX 4060/5070 Ti Overclocked and Undervoltaged.
+At hour 14, this threw OOM errors until I fixed the 83°C thermal throttling with a paste replacement.
+Advised is to use Watercooled setup.
+"""
+
+import torch
+import deepspeed
+from transformers import (
+    AutoModelForCausalLM, 
+    AutoTokenizer,
+    TrainingArguments,
+    Trainer
+)
+from peft import LoraConfig, get_peft_model, TaskType
+import argparse
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_name", type=str, default="Qwen/Qwen2.5-7B-Instruct")
+    parser.add_argument("--output_dir", type=str, default="./output")
+    parser.add_argument("--deepspeed", type=str, default=None)
+    args = parser.parse_args()
+    
+    # Load base model. I use 4-bit NF4 with double quantization to fit the 8GB 4060.
+    # The 5070 Ti handles the heavier loads but sits idle 30% of the time waiting for the 4060.
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_name,
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+        torch_dtype=torch.bfloat16,
+        device_map="auto"  # DeepSpeed ZeRO-2 handles the asymmetric VRAM (8GB + 16GB)
+    )
+    
+    # LoRA adapters for the verification heads (local at layer 7, arbitration at 14, global at 28).
+    # I tried rank 128 first but it OOM'd on the 4060, so I dropped to 64.
+    lora_config = LoraConfig(
+        r=64,
+        lora_alpha=16,
+        target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+        lora_dropout=0.05,
+        bias="none",
+        task_type=TaskType.CAUSAL_LM
+    )
+    model = get_peft_model(model, lora_config)
+    
+    # Tokenizer setup
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    # Training arguments.
+    # I wasted two days on pipeline parallelism before switching to ZeRO-2.
+    # This config ran for 72 hours straight with 50,000 samples distilled from DeepSeek-V2.
+    training_args = TrainingArguments(
+        output_dir=args.output_dir,
+        num_train_epochs=3,
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=2,
+        learning_rate=2e-4,
+        warmup_steps=500,
+        logging_steps=10,
+        save_steps=500,
+        bf16=True,
+        deepspeed=args.deepspeed,
+        gradient_checkpointing=True,
+        optim="adamw_torch"
+    )
+    
+    print("Model loaded. Ready for training.")
+    print(f"Trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+    print("Warning: This is a simplified skeleton. I trained for 72h on 50k samples.")
+    print("Watch your thermals. I hit 83°C at hour 14 and had to repaste.")
+
+if __name__ == "__main__":
+    main()