🧠 LoRA Support #37

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opened 2025-06-07 20:07:03 -04:00 by milo · 0 comments

milo commented

2025-06-07 20:07:03 -04:00

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📌 Objective

Enable the bot to load and use LoRA (Low-Rank Adapter) weights on top of a base model (e.g. LLaMA3 or DeepSeek) to inject stronger personality, memory, or domain-specific behavior without retraining the entire model.

✅ Phase 1: Add LoRA Config

Extend settings.yml:

ai:
  use_lora: true
  lora_path: "./loras/gojo"

Optionally allow override via environment variable:
```
LORA_PATH=./loras/gojo
```

⚙️ Phase 2: Update AI Backend

Modify ai.py to:
- Load lora_path if use_lora: true
- When calling /generate (or /modelfile), include:
```
{
  "model": "llama3",
  "lora": "gojo",
  "prompt": "..."
}
```
- Validate that the LoRA exists (may require preload or pull)
- Add fallback log if not found

🛠️ Phase 3: Add CLI or Admin Commands (Optional)

!lora enable gojo
!lora disable
!lora list (if supported by backend)
!lora status

🧪 Phase 4: LoRA Management + Testing

Set up a test LoRA directory (e.g. ./loras/gojo)
Confirm inference works with:
- Ollama modfile using ADAPTER line:
```
FROM llama3
ADAPTER ./loras/gojo
```
- Or by passing lora field to /generate

📁 Directory Structure Example

/src/
  └── ai.py
/loras/
  └── gojo/
      ├── adapter_config.json
      └── adapter_model.safetensors

🧩 Future Possibilities

Train your own LoRA (requires datasets + fine-tuning script)
Use peft, qlora, or transformers with tools like:
- Axolotl
- text-generation-webui
- lm-studio (preview LoRAs)
Host custom LoRA as an API endpoint

🚀 Benefit Summary

Way stronger character fidelity (esp. for anime, roleplay, or creators)
Switch between personas without switching base models
Smaller size + faster than full fine-tuning

### 📌 Objective Enable the bot to load and use **LoRA (Low-Rank Adapter)** weights on top of a base model (e.g. LLaMA3 or DeepSeek) to inject stronger personality, memory, or domain-specific behavior without retraining the entire model. --- ### ✅ Phase 1: Add LoRA Config - [ ] Extend `settings.yml`: ```yaml ai: use_lora: true lora_path: "./loras/gojo" ``` - [ ] Optionally allow override via environment variable: ```env LORA_PATH=./loras/gojo ``` --- ### ⚙️ Phase 2: Update AI Backend - [ ] Modify `ai.py` to: - Load `lora_path` if `use_lora: true` - When calling `/generate` (or `/modelfile`), **include**: ```json { "model": "llama3", "lora": "gojo", "prompt": "..." } ``` - Validate that the LoRA exists (may require preload or pull) - Add fallback log if not found --- ### 🛠️ Phase 3: Add CLI or Admin Commands (Optional) - `!lora enable gojo` - `!lora disable` - `!lora list` *(if supported by backend)* - `!lora status` --- ### 🧪 Phase 4: LoRA Management + Testing - [ ] Set up a test LoRA directory (e.g. `./loras/gojo`) - [ ] Confirm inference works with: - Ollama modfile using `ADAPTER` line: ```mod FROM llama3 ADAPTER ./loras/gojo ``` - Or by passing `lora` field to `/generate` --- ### 📁 Directory Structure Example ``` /src/ └── ai.py /loras/ └── gojo/ ├── adapter_config.json └── adapter_model.safetensors ``` --- ### 🧩 Future Possibilities - Train your own LoRA (requires datasets + fine-tuning script) - Use `peft`, `qlora`, or `transformers` with tools like: - `Axolotl` - `text-generation-webui` - `lm-studio` (preview LoRAs) - Host custom LoRA as an API endpoint --- ### 🚀 Benefit Summary - Way stronger character fidelity (esp. for anime, roleplay, or creators) - Switch between personas without switching base models - Smaller size + faster than full fine-tuning