| --- |
| base_model: unsloth/Meta-Llama-3.1-8B-Instruct |
| library_name: peft |
| license: llama3.1 |
| tags: |
| - sevzero |
| - openenv |
| - sft |
| - lora |
| - sre |
| --- |
| |
| # SevZero SFT-primary adapter |
|
|
| LoRA adapter for `unsloth/Meta-Llama-3.1-8B-Instruct`, trained for the SevZero OpenEnv India Hackathon 2026 submission. |
|
|
| ## What this is |
|
|
| This is the supervised fine-tuning stage of the SevZero pipeline. It was trained on curated incident-response trajectories collected from frontier teachers against the SevZero SRE simulator. |
|
|
| - Base model: `unsloth/Meta-Llama-3.1-8B-Instruct` |
| - Training stack: `transformers + peft + trl.SFTTrainer` |
| - Adapter: LoRA, rank around 64 over attention and MLP modules |
| - Precision: bf16 adapter training on a single H200 HF Job |
| - Steps: 200 |
| - Learning rate: `1e-5` |
| - Max sequence length: 1024 |
|
|
| Unsloth is used here as the ungated base-model mirror. The GRPO stage did not use Unsloth as the trainer. |
|
|
| ## Eval summary |
|
|
| Held-out seeds: `13`, `99`, `777`. Tasks: Easy, Medium, Hard. |
|
|
| | Model | Easy | Medium | Hard | Mean | |
| |---|---:|---:|---:|---:| |
| | Untrained Llama-3.1-8B-Instruct | 0.8199 | 0.9419 | 0.6369 | 0.7996 | |
| | SFT-primary | 0.8199 | 0.9419 | 0.6269 | 0.7962 | |
|
|
| SFT improved formatting and tool-call priors, but did not improve held-out decision quality. That flat result is discussed in the SevZero blog. |
|
|
| ## Links |
|
|
| - Environment Space: https://huggingface.co/spaces/Mist-ic/sevzero-env |
| - Blog: https://huggingface.co/spaces/Mist-ic/sevzero-env/blob/main/BLOG.md |
| - Eval dataset: https://huggingface.co/datasets/Mist-ic/sevzero-eval-results |
| - Training data: https://huggingface.co/datasets/Mist-ic/sevzero-expert-trajectories |
|
|
