---
library_name: peft
base_model: meta-llama/Llama-3.1-8B
tags:
- lora
- transformers
pipeline_tag: text-generation
---

# llama31-8b-knapsack-lora-persistent

Anonymous supplementary release for a double-blind workshop submission. This is
one of four LoRA adapters (Mistral-7B-v0.3 / Llama-3.1-8B base model x
persistent/stateless training regime) fine-tuned on the Opaque Knapsack
agentic task, extending a prior single-base-model result (see the sibling
Qwen3-8B release) to a second base model family for the same reproducibility
review.

- **Base model:** [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
- **Training regime:** persistent (trained with a persistent Python interpreter runtime (state carries over across agent turns))
- **Seed:** 3407

## Training configuration

Fine-tuned with [Axolotl](https://github.com/axolotl-ai-cloud/axolotl), LoRA
adapter, 4-bit NF4 quantized base:

| Hyperparameter | Value |
|---|---|
| lora_r | 64 |
| lora_alpha | 128 |
| lora_dropout | 0.05 |
| lora_target_modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| learning_rate | 1e-4 |
| lr_scheduler | cosine |
| optimizer | adamw_torch |
| epochs | 3.0 |
| micro_batch_size | 1 |
| gradient_accumulation_steps | 16 |
| sequence_len | 16384 |
| sample_packing | false |
| seed | 3407 |
| training data | paired traces for the "persistent" regime (see paper Appendix for pairing/filtering procedure) |

Base checkpoint's own chat-format tokens (e.g. `<|eot_id|>`) are untrained on this base (non-instruct) checkpoint -- trained and served with a hand-written minimal template using only real trained tokens (BOS/EOS + plain-text role prefixes), not Llama's native instruct template.

## Provenance

Released anonymously alongside a NeurIPS workshop submission for
reproducibility review. Non-anonymous release (paper citation, full code, full
training traces) will follow after the review process concludes.