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psse-agent-gpt-oss-20b-lora
harshith0214/psse-agent-gpt-oss-20b-lora
psse-agent-gpt-oss-20b-lora is a text generation model from harshith0214. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as other.
A LoRA adapter fine-tuned on GPT-OSS 20B for automated Power System State Estimation (PSSE) fault diagnosis.
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- other
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026
- SHA
- 797c40f9
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Base models
Model card
From the Hugging Face model README
PSSE Agent — GPT-OSS 20B LoRA (v2)
A LoRA adapter fine-tuned on GPT-OSS 20B for automated Power System State Estimation (PSSE) fault diagnosis.
Given a power system snapshot and WLS state estimation outputs (residuals r, normalized Lagrange multipliers λN), the model classifies the fault into one of four categories with high confidence.
Model Details
| Property | Value |
|---|---|
| Base model | unsloth/gpt-oss-20b-unsloth-bnb-4bit |
| Architecture | GPT-OSS 20B (32-expert MoE) |
| Adapter type | LoRA (PEFT) |
| LoRA rank / alpha | 64 / 64 |
| Target modules | q/k/v/o/gate/up/down projections + MoE experts |
| Trainable parameters | 31.85M / 20.9B (0.15%) |
| Training epochs | 3 |
| Final train loss | 0.005 |
| Final eval loss | 0.005 |
| Peak GPU memory | 56 GB (H100 80GB) |
| Quantization | 4-bit (QLoRA) |
Task
The model acts as a power-system diagnostic agent using an agentic tool-call loop:
- Calls
wls_from_pathon the provided snapshot - Inspects residuals
rand Lagrange multipliersλN - Optionally calls correction tools (
correct_measurements_from_path,correct_parameters_from_path,correct_topology_from_path) - Outputs a structured JSON verdict
Output Format
{
"has_error": true,
"error_family": "parameter_error",
"suspect_location": {"line_row": 5, "from_bus": 3, "to_bus": 4},
"recommended_tool": "correct_parameters_from_path",
"confidence": 0.99
}
Error Classes
| Class | Description |
|---|---|
measurement_error | Bad sensor reading — concentrated large residuals at specific channels |
parameter_error | Incorrect line parameters — large Lagrange multipliers on a specific branch |
topology_error | Incorrect network topology — widespread residual pattern |
no_error | System healthy — residuals within normal bounds |
Evaluation Results (held-out test set, IEEE 14-bus)
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| measurement_error | 100.0% | 100.0% | 100.0% | 75 |
| no_error | 100.0% | 100.0% | 100.0% | 75 |
| parameter_error | 100.0% | 100.0% | 100.0% | 75 |
| topology_error | 100.0% | 100.0% | 100.0% | 71 |
| macro avg | 100.0% | 100.0% | 100.0% | 296 |
- Overall accuracy: 296/296 (100%)
- Mean confidence: 0.9875
- Null predictions (parse failures): 0/296
Note: Evaluated on held-out samples from the IEEE 14-bus (
case14) synthetic dataset. Out-of-distribution generalization to other grid topologies has not been evaluated.
Training Data
- Source: Synthetically generated power system diagnostic traces (
case14, IEEE 14-bus system) - Total samples: 1,978 (deduplicated on user snapshot)
- Split: 70% train (1,400) / 15% val (297) / 15% test (296)
- Split strategy: Stratified by
error_family × tool_sequence, seed=42 - Format: GPT-OSS channel format (
commentary{}/final{})
Usage
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="harshith0214/psse-agent-gpt-oss-20b-lora",
max_seq_length=16384,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
Training Infrastructure
- Hardware: NVIDIA H100 80GB HBM3
- Framework: Unsloth + HuggingFace TRL (SFTTrainer)
- Optimizer: AdamW 8-bit, cosine LR schedule
- Learning rate: 1e-4, warmup 50 steps
- Batch size: 4 × 4 gradient accumulation = 16 effective
Limitations
- Trained and evaluated exclusively on the IEEE 14-bus (
case14) synthetic test system - Performance on larger or real-world grid topologies is untested
- Requires the GPT-OSS base model and Unsloth for inference
- Task
- Text Generation
- Library
- peft
- License
- other
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026