| --- |
| license: llama3.1 |
| base_model: NousResearch/Meta-Llama-3.1-8B-Instruct |
| library_name: peft |
| tags: |
| - rail |
| - track-geometry |
| - lora |
| - defect-detection |
| - 49-cfr-213 |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| # RTG-Llama (Rail Track Geometry — Llama 3.1-8B LoRA) |
|
|
| A LoRA adapter on top of **Llama 3.1-8B-Instruct**, fine-tuned to write |
| five-section defect-investigation reports for rail track-geometry segments |
| governed by **49 CFR Part 213, Class 3**. Pairs with a separate VAE detector |
| and SHAP attribution module in the parent project; this repository contains |
| only the language-model adapter. |
|
|
| Part of the **RTG framework** (VAE detection + SHAP + RAG + RTG-Llama |
| report generation). See the project repository at |
| [Abelar-Ai/rail_llm](https://github.com/Abelar-Ai/rail_llm). |
|
|
| ## Training summary |
|
|
| | | | |
| |---|---| |
| | Base model | `NousResearch/Meta-Llama-3.1-8B-Instruct` | |
| | Adapter type | LoRA (PEFT 0.19.1) | |
| | Rank `r` | 16 | |
| | `lora_alpha` | 32 | |
| | Dropout | 0.05 | |
| | Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` | |
| | Task | Causal LM (SFT, 3 epochs) | |
| | Training pairs | Defect reports paired with case payloads (VAE output, FRA limit check, SHAP weights, raw values) | |
|
|
| ## Loading |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| BASE = "meta-llama/Llama-3.1-8B-Instruct" # or NousResearch/Meta-Llama-3.1-8B-Instruct |
| ADAPTER = "BenGyi/rtg-llama-lora" |
| |
| tokenizer = AutoTokenizer.from_pretrained(BASE) |
| base = AutoModelForCausalLM.from_pretrained( |
| BASE, |
| torch_dtype=torch.float32, # see note on dtype below |
| device_map="auto", |
| low_cpu_mem_usage=True, |
| ) |
| model = PeftModel.from_pretrained(base, ADAPTER) |
| model.eval() |
| ``` |
|
|
| ## Prompt format |
|
|
| The adapter expects a structured case payload as input. Minimal example: |
|
|
| ```python |
| prompt = ( |
| "You are a rail track-geometry maintenance reviewer. Produce a structured " |
| "defect report with exactly the following sections: " |
| "1. Justification, 2. Explainability, 3. Technical Explanation, " |
| "4. Maintenance Recommendation, 5. References.\n\n" |
| "Case payload:\n" |
| " VAE reconstruction error: 23.85 (threshold τ = 1.767)\n" |
| " Verdict: DEFECTIVE\n" |
| " Raw values (inches):\n" |
| " LProf62=0.310, RProf62=0.468, LAlign62=0.921, RAlign62=0.665,\n" |
| " Gage=56.479, Crosslevel=5.220\n" |
| " FRA Class 3 check: Crosslevel exceeds ±1.75 in limit.\n" |
| " Top SHAP drivers: Crosslevel (+17.64), LAlign62 (+2.29), " |
| "RProf62 (+1.19).\n" |
| ) |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| out = model.generate(**inputs, max_new_tokens=900, do_sample=False) |
| print(tokenizer.decode(out[0], skip_special_tokens=True)) |
| ``` |
|
|
| For best results, ground the prompt in retrieved 49 CFR §213 passages |
| (see the `RAG` setup in the parent repository). |
|
|
| ## Dtype note |
|
|
| On **Blackwell GPUs (sm_120, e.g. RTX 5090)** the model must be loaded in |
| **fp32**: bf16/fp16 attention overflows on this architecture and produces |
| NaN logits. On Hopper/Ada (A100, H100, RTX 4090) bf16 works and is faster. |
| |
| ## License |
| |
| Inherits **Llama 3.1 Community License** from the base model. You must |
| agree to the Llama 3.1 license to use this adapter. |
| |
| ## Citation |
| |
| If you use this adapter, please cite the paper accompanying the project |
| (see the GitHub repository for the current reference). |
