Update SETUP_MIAAI.md: add bare Ubuntu rebuild section (driver, packages, Ollama)

SETUP_MIAAI.md

@@ -9,12 +9,61 @@
 
 ---
 
-## Pre-Training Checklist (every time)
+## Starting from Bare Ubuntu 25.10
 
-Before starting a training run, verify these:
+If rebuilding from scratch, complete these steps first before anything else.
+
+### A. System packages
+```bash
+sudo apt update && sudo apt upgrade -y
+sudo apt install -y \
+  build-essential cmake git curl wget \
+  python3-dev libssl-dev zlib1g-dev \
+  ca-certificates gnupg lsb-release
+```
+
+### B. NVIDIA driver (580.xx)
+Ubuntu 25.10 is too new for NVIDIA's apt repo. Install via ubuntu-drivers:
+```bash
+sudo ubuntu-drivers autoinstall
+sudo reboot
+```
+
+After reboot, verify:
+```bash
+nvidia-smi
+# Must show: NVIDIA GeForce RTX 5080, Driver Version: 580.x
+```
+
+If ubuntu-drivers installs the wrong version, force the right one:
+```bash
+sudo apt install -y nvidia-driver-580
+sudo reboot
+```
+
+### C. Install Ollama
+```bash
+curl -fsSL https://ollama.com/install.sh | sh
+
+# Verify it's running
+systemctl status ollama
+```
+
+### D. HuggingFace CLI
+```bash
+pip3 install huggingface_hub
+huggingface-cli login
+# Paste your HF token — required for gated models like meta-llama
+```
+
+Once steps A–D are done, continue with the One-time Setup below.
+
+---
+
+## Pre-Training Checklist (every session)
 
 ```bash
-# 1. Stop Ollama — if a request hits it mid-training it will compete for VRAM
+# 1. Stop Ollama — if it receives a request mid-training it will compete for VRAM
 sudo systemctl stop ollama
 
 # 2. Activate conda env
@@ -26,7 +75,7 @@ export CUDA_HOME=$CONDA_PREFIX
 export PATH=$CUDA_HOME/bin:$PATH
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 
-# 4. Confirm GPU is clear (should show no processes)
+# 4. Confirm GPU is clear (should show no processes before training)
 nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
 
 # 5. Go to axolotl directory
@@ -43,18 +92,18 @@ axolotl train ~/human_chat_qlora.yml
 # Restart Ollama
 sudo systemctl start ollama
 
-# Test the adapter
+# Test the adapter interactively
 axolotl inference ~/human_chat_qlora.yml \
   --lora-model-dir ~/outputs/llama31-8b-humanchat \
   --prompter chat
 
-# (Optional) Merge adapter into base model
+# (Optional) Merge adapter into base model for standalone deployment
 axolotl merge-lora ~/human_chat_qlora.yml
 ```
 
 ---
 
-## One-time Setup (fresh machine only)
+## One-time Setup (fresh machine — after bare Ubuntu steps above)
 
 ### 1. Install Miniconda
 ```bash
@@ -115,16 +164,17 @@ Must compile from source with a patched CMakeLists.txt.
 
 ```bash
 # Clone bitsandbytes v0.49.1
-git clone --branch v0.49.1 --depth 1 https://github.com/bitsandbytes-foundation/bitsandbytes.git /tmp/bnb_0491
+git clone --branch v0.49.1 --depth 1 \
+  https://github.com/bitsandbytes-foundation/bitsandbytes.git /tmp/bnb_0491
 
 # Patch CMakeLists.txt: insert sm_120 override before the foreach loop
 # (cmake >= 3.23.0 uses its own built-in arch list which does not include sm_120)
 sed -i '/    foreach(capability \${CMAKE_CUDA_ARCHITECTURES_ALL})/i\    # RTX 5080 sm_120 patch\n    set(CMAKE_CUDA_ARCHITECTURES_ALL 120)' /tmp/bnb_0491/CMakeLists.txt
 
-# Verify patch landed correctly (should show the set() line immediately before foreach)
+# Verify patch landed correctly — set() line must appear immediately before foreach
 grep -n "ARCHITECTURES_ALL\|foreach" /tmp/bnb_0491/CMakeLists.txt | tail -5
 
-# Configure
+# Configure — must point cmake at conda's nvcc explicitly
 cmake \
   -DCMAKE_CUDA_COMPILER=/opt/miniconda3/envs/axolotl/bin/nvcc \
   -DCOMPUTE_BACKEND=cuda \
@@ -132,13 +182,14 @@ cmake \
   -B /tmp/bnb_0491/build 2>&1 | grep -E "(Capabilit|CUDA Ver|Error)"
 # Must show: CUDA Capabilities Selected: 120
 
-# Build
+# Build (adjust -j to your CPU core count)
 cmake --build /tmp/bnb_0491/build -j10
 
 # Install into conda site-packages
-cp -r /tmp/bnb_0491/bitsandbytes /opt/miniconda3/envs/axolotl/lib/python3.11/site-packages/
+cp -r /tmp/bnb_0491/bitsandbytes \
+  /opt/miniconda3/envs/axolotl/lib/python3.11/site-packages/
 
-# Verify
+# Verify CUDA works
 python3 -c "
 import torch, bitsandbytes as bnb
 x = torch.randn(64, 64, device='cuda')
@@ -147,25 +198,34 @@ print('bitsandbytes CUDA OK:', l(x).shape)
 "
 ```
 
-### 9. HuggingFace login (meta-llama is gated)
+### 9. Copy training config to home
 ```bash
-huggingface-cli login
+cp /home/tocmo0nlord/axolotl/human_chat_qlora.yml ~/human_chat_qlora.yml
-# Paste your HF token when prompted
 ```
 
-### 10. Verify everything is working
+### 10. Verify the full stack
 ```bash
 python3 -c "
-import torch, bitsandbytes as bnb, flash_attn, transformers, axolotl
+import torch, bitsandbytes as bnb, flash_attn, transformers
-print('torch:', torch.__version__, '| CUDA:', torch.version.cuda)
+print('torch      :', torch.__version__, '| CUDA:', torch.version.cuda)
 print('bitsandbytes:', bnb.__version__)
-print('flash_attn:', flash_attn.__version__)
+print('flash_attn :', flash_attn.__version__)
 print('transformers:', transformers.__version__)
-print('GPU:', torch.cuda.get_device_name(0))
+print('GPU        :', torch.cuda.get_device_name(0))
-print('VRAM:', round(torch.cuda.get_device_properties(0).total_memory/1e9, 1), 'GB')
+print('VRAM       :', round(torch.cuda.get_device_properties(0).total_memory/1e9, 1), 'GB')
 "
 ```
 
+Expected output:
+```
+torch      : 2.x.x | CUDA: 13.2
+bitsandbytes: 0.50.0.dev0
+flash_attn : 2.x.x
+transformers: 5.x.x
+GPU        : NVIDIA GeForce RTX 5080
+VRAM       : 16.3 GB
+```
+
 ---
 
 ## Training Config — human_chat_qlora.yml
@@ -176,7 +236,7 @@ Key settings tuned for RTX 5080 (16GB):
 |---|---|---|
 | `sequence_len` | `2048` | 4096 OOMs during loss computation (logits x 128k vocab) |
 | `micro_batch_size` | `1` | Effective batch = micro x grad_accum = 8 |
-| `gradient_accumulation_steps` | `8` | Keeps effective batch at 8 |
+| `gradient_accumulation_steps` | `8` | Keeps effective batch size at 8 |
 | `adapter` | `qlora` | 4-bit via bitsandbytes compiled from source |
 | `attn_implementation` | `flash_attention_2` | Not the deprecated `flash_attention: true` |
 | `type` (datasets) | `chat_template` | Not the deprecated `sharegpt` |
@@ -187,8 +247,7 @@ Expected training metrics (RTX 5080, ~65k samples, 2 epochs):
 - Initial eval loss: ~0.81, perplexity ~2.25
 - Final loss target: ~0.55–0.60
 
-To use more VRAM (~14GB) and improve gradient signal, increase `micro_batch_size: 2`
+To push VRAM to ~14GB and improve training: set `micro_batch_size: 2` and `gradient_accumulation_steps: 4`.
-(adjust `gradient_accumulation_steps: 4` to keep effective batch at 8).
 
 ---
 
@@ -203,11 +262,12 @@ To use more VRAM (~14GB) and improve gradient signal, increase `micro_batch_size
 | `torchaudio` not found for cu132 | No cu132 wheel exists | Skip torchaudio — not needed |
 | flash-attn compile is slow | Single-threaded by default | Set `MAX_JOBS=<cpu_count>` before pip install |
 | `nvcc fatal: Unsupported gpu architecture 'compute_50'` | bitsandbytes CMakeLists.txt hardcodes sm_50; CUDA 13.2 dropped it | Patch CMakeLists.txt (see step 8 above) |
-| `CUDA Capabilities Selected: 50;52;...` (ignores -D flag) | cmake >= 3.23 built-in arch list lacks sm_120; CMakeLists.txt overrides -D | Insert `set(CMAKE_CUDA_ARCHITECTURES_ALL 120)` before foreach loop |
+| `CUDA Capabilities Selected: 50;52;...` ignores -D flag | cmake >= 3.23 built-in arch list lacks sm_120; CMakeLists.txt overrides -D | Insert `set(CMAKE_CUDA_ARCHITECTURES_ALL 120)` before foreach loop |
 | `BackendUnavailable: scikit_build_core` | pip install of bnb triggers cmake rebuild | Copy .so directly to site-packages instead |
 | `torch.OutOfMemoryError` during eval | logits tensor (batch x 4096 x 128k vocab) too large | Set `sequence_len: 2048`, `micro_batch_size: 1` |
 | `type: sharegpt` deprecation warning | axolotl removed sharegpt type | Use `type: chat_template` with field mappings |
 | `flash_attention: true` deprecation | Old config key removed | Use `attn_implementation: flash_attention_2` |
 | Capybara dataset `field_messages null` | Capybara uses input/output format, not conversations | Switch to SlimOrca or OpenHermes-2.5 |
 | Ollama loads model mid-training | Ollama is enabled and receives a request | `sudo systemctl stop ollama` before training |
-| Training slower than expected (~3.5h not 19min) | The fast it/s on screen is the eval loop, not training | Normal — training includes backward pass and optimizer |
+| Training much slower than eval speed | The fast it/s on screen is the eval loop (forward only) | Normal — training includes backward pass and optimizer (~3.5h total) |
+| ubuntu-drivers installs wrong NVIDIA version | Multiple driver candidates available | Force with `apt install nvidia-driver-580` |