# Axolotl Setup — miaai (RTX 5080, CUDA 13.2)

## System Info
- GPU: NVIDIA RTX 5080 (16GB VRAM, sm_120 / Blackwell)
- Driver: 580.126.09 — max CUDA 13.0 shown by nvidia-smi, but nvcc from conda is 13.2
- OS: Ubuntu 25.10 (Python 3.13 system — do NOT use system Python for ML)
- Axolotl branch: `activeblue/main`

---

## One-time Setup

### 1. Install Miniconda
```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p /opt/miniconda3
/opt/miniconda3/bin/conda init bash
source ~/.bashrc
```

### 2. Create Python 3.11 environment
```bash
conda create -n axolotl python=3.11 -y
conda activate axolotl
```

### 3. Clone and sync repo with upstream
```bash
git clone https://git.activeblue.net/tocmo0nlord/axolotl.git
cd axolotl
git remote add upstream https://github.com/axolotl-ai-cloud/axolotl.git
git fetch upstream
git rebase upstream/main        # keeps activeblue patches on top
git push origin activeblue/main --force-with-lease
```

### 4. Install CUDA toolkit (needed to compile flash-attn and bitsandbytes)
```bash
conda install -y -c "nvidia/label/cuda-12.8.0" cuda-toolkit
export CUDA_HOME=$CONDA_PREFIX
export PATH=$CUDA_HOME/bin:$PATH
```

> NOTE: Despite installing from the cuda-12.8.0 channel, conda resolves nvcc to **13.2.78**.
> This is fine — use cu132 everywhere to match.

### 5. Install PyTorch — use cu132 (matches nvcc from conda)
> NOTE: torchaudio has no cu132 wheel — skip it, not needed for LLM training
```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu132
python -c "import torch; print('CUDA:', torch.version.cuda); print('GPU:', torch.cuda.get_device_name(0))"
```

### 6. Install Axolotl
```bash
pip install -e "."
```

> **flash-attn compiles CUDA kernels from source — takes 15–25 min on 10 cores of i7-14700K.**
> Always set `MAX_JOBS` to the number of available CPU cores:
```bash
MAX_JOBS=10 pip install flash-attn --no-build-isolation
```

### 7. Compile bitsandbytes from source for sm_120 (RTX 5080 / Blackwell)

The prebuilt bitsandbytes wheels do not include sm_120 support and CUDA 13.2 dropped sm_50–53.
You 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
cd /tmp/bnb_0491

# Patch CMakeLists.txt: override arch list to sm_120 only, just before the foreach loop
# (cmake >= 3.23.0 skips the manual arch block and uses its own built-in list which lacks sm_120)
sed -i '/    foreach(capability \${CMAKE_CUDA_ARCHITECTURES_ALL})/i\    # RTX 5080 sm_120 patch: override before capability list is built\n    set(CMAKE_CUDA_ARCHITECTURES_ALL 120)' CMakeLists.txt

# Verify the patch landed at the right line
grep -n "ARCHITECTURES_ALL\|foreach" CMakeLists.txt | tail -5
# Should show: set(CMAKE_CUDA_ARCHITECTURES_ALL 120) immediately before the foreach line

# Configure — must point cmake at conda's nvcc
cmake \
  -DCMAKE_CUDA_COMPILER=/opt/miniconda3/envs/axolotl/bin/nvcc \
  -DCOMPUTE_BACKEND=cuda \
  -S /tmp/bnb_0491 \
  -B /tmp/bnb_0491/build 2>&1 | grep -E "(Capabilit|CUDA Ver|Error)"
# Expected: "CUDA Capabilities Selected: 120"

# Build (j10 uses 10 cores — adjust to your CPU)
cmake --build /tmp/bnb_0491/build -j10

# Install into conda site-packages
SITE_PKG=/opt/miniconda3/envs/axolotl/lib/python3.11/site-packages
cp -r /tmp/bnb_0491/bitsandbytes "$SITE_PKG/"

# Verify
python3 -c "
import torch, bitsandbytes as bnb
x = torch.randn(64, 64, device='cuda')
l = bnb.nn.Linear8bitLt(64, 64).cuda()
print('bitsandbytes CUDA OK:', l(x).shape)
"
```

---

## Every Session (after first-time setup)
```bash
export PATH="/opt/miniconda3/bin:$PATH"
conda activate axolotl
export CUDA_HOME=$CONDA_PREFIX
export PATH=$CUDA_HOME/bin:$PATH
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
cd /home/tocmo0nlord/axolotl
```

---

## Training Config — human_chat_qlora.yml

Key settings that work on RTX 5080 (16GB):

| Setting | Value | Notes |
|---|---|---|
| `sequence_len` | `2048` | 4096 causes OOM during loss computation (logits x 128k vocab) |
| `micro_batch_size` | `1` | Keep low; effective batch = micro x grad_accum |
| `gradient_accumulation_steps` | `8` | Effective batch = 8 |
| `adapter` | `qlora` | QLoRA 4-bit via bitsandbytes |
| `attn_implementation` | `flash_attention_2` | Not the deprecated `flash_attention: true` |
| `type` (datasets) | `chat_template` | Not the deprecated `sharegpt` |

Dataset fields for SlimOrca / OpenHermes-2.5 (sharegpt-format with different field names):
```yaml
datasets:
  - path: Open-Orca/SlimOrca
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    split: "train[:3%]"
```

## Run Training
```bash
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
axolotl train ~/human_chat_qlora.yml
```

Expected startup sequence:
1. Config validation + capability detection (shows `sm_120`)
2. Dataset tokenization (~65k samples, ~30 seconds)
3. `Loading weights: 100% 291/291`
4. `trainable params: 167,772,160 || all params: 8,198,033,408 || trainable%: 2.05`
5. Initial eval: loss ~0.81, perplexity ~2.25, VRAM ~8.5GB
6. Training steps at ~2.6 it/s, VRAM ~9-10GB

---

## Common Pitfalls Encountered

| Problem | Cause | Fix |
|---|---|---|
| `externally-managed-environment` | System Python 3.13 blocks pip | Use conda env, never system pip |
| `No module named torch` (flash-attn) | pip builds in isolated env | Use `--no-build-isolation` |
| `CUDA_HOME not set` | CUDA toolkit not installed | `conda install cuda-toolkit` from nvidia channel |
| `CUDA version mismatch 13.2 vs 12.8` | Conda nvcc is 13.2, torch was cu128 | Reinstall torch with `--index-url .../cu132` |
| `torchaudio` not found for cu132 | No cu132 wheel exists | Skip torchaudio — not needed |
| `src refspec main does not match` | Fork default branch is `activeblue/main` | `git push origin activeblue/main` |
| 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 7 above) |
| `CUDA Capabilities Selected: 50;52;...` (ignores sm_120) | cmake >= 3.23 built-in arch list lacks sm_120 | Add `set(CMAKE_CUDA_ARCHITECTURES_ALL 120)` before foreach loop |
| `BackendUnavailable: scikit_build_core` | pip install of bnb tries to 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 |
