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Model: manuelaschrittwieser/llama-3-invoice-extractor-merged
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: meta-llama/Meta-Llama-3-8B
+library_name: unsloth
+tags:
+- finance
+- information-extraction
+- structured-data
+- json
+- merged
+- llama-3
+datasets:
+- manuelaschrittwieser/invoice-extraction-dataset-v2
+metrics:
+- train_loss
+model-index:
+- name: Llama-3-8B Invoice Extractor (Merged)
+  results:
+  - task:
+      type: text-generation
+      name: Information Extraction
+    dataset:
+      name: invoice-extraction-dataset-v2
+      type: manuelaschrittwieser/invoice-extraction-dataset-v2
+    metrics:
+    - name: Final Train Loss
+      type: loss
+      value: 0.2555
+pipeline_tag: text-generation
+finetuned_from: meta-llama/Meta-Llama-3-8B
+---
+
+# 📑 Llama-3-8B Invoice Extractor (Merged)
+
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## 1. Model Description & Intended Use
+This model is a fine-tuned version of **Meta's Llama-3-8B**, specifically optimized for **Structured Information Extraction**.
+It is designed to act as a "Parser Agent" that transforms messy, unstructured financial text—such as invoice descriptions, receipt notes, and purchase logs—into machine-readable, valid JSON objects.
+
+**Primary Intended Use:**
+- Automated accounting and bookkeeping workflows.
+- Extracting data from OCR-processed receipts.
+- Building serverless financial data pipelines.
+
+**Target Schema:**
+```json
+{
+  "item": "string",
+  "quantity": "integer",
+  "date": "string",
+  "vendor": "string",
+  "total": "float",
+  "currency": "string"
+}
+```
+
+## 2. Training Data Information
+
+The model was trained on the `manuelaschrittwieser/invoice-extraction-dataset-v2`, which consists of **601 high-quality examples**.
+
+* **Methodology:** The dataset uses a synthetic generation pipeline with 7 distinct sentence templates to ensure robustness against different linguistic structures.
+* **Diversity:** Includes 20+ vendors, 20+ item categories, and 5 currency types (USD, EUR, GBP, CAD, JPY).
+* **Format:** The data was prepared in an instruction-input-output format to reinforce strict adherence to the JSON schema.
+
+## 3. Training Procedure & Hyperparameters
+
+The training was conducted using **Parameter-Efficient Fine-Tuning (PEFT)** with the **QLoRA** method via the **Unsloth** library,
+which significantly reduced VRAM usage while maintaining performance.
+
+### Hyperparameters:
+
+* **Epochs:** Custom step-based (120 global steps)
+* **Learning Rate:** 2e-4
+* **Batch Size:** 2 (with Gradient Accumulation Steps = 4)
+* **Optimizer:** AdamW (8-bit)
+* **Learning Rate Scheduler:** Linear
+* **LoRA Rank (r):** 16
+* **LoRA Alpha:** 16
+* **Precision:** 4-bit Quantization (merged into 16-bit for this standalone version)
+
+## 4. Evaluation Results
+
+### Baseline (Llama-3-8B) vs. Fine-Tuned
+| **Feature**  | **Baseline Model** | **Fine-Tuned Model (v2)** |
+| ------------- |:-------------:| :-------------:|
+| **Output Format** | Conversational / Explanatory | **Strict JSON Only** |
+| **JSON Validity** | Often fails (includes extra text) | **100% Valid JSON** in test runs |
+| **Entity Recognition** | High accuracy, but low precision | **High Precision** (mapped to schema) |
+| **Instruction Following** | Moderate | **High** (Stays within Response block) |
+
+**Tracking:** Training performance was monitored via **Weights & Biases (W&B)**, showing a consistent reduction in loss over 120 steps without signs of overfitting.
+
+## 5. Limitations & Known Issues
+
+* **Language:** Optimized primarily for English. Performance on other languages is not guaranteed.
+* **Hallucination:** While the model is highly structured, it can occasionally misinterpret dates if they are provided in ambiguous formats (e.g., 01/02/03).
+* **Context Length:** Best performance is achieved with input lengths under 512 tokens.
+* **Verification:** Users should implement a JSON validation layer in production to handle the rare cases of malformed output.
+
+## 6. Code Example: Loading and Usage
+
+Since this is a merged model, it can be used with standard `transformers` or `vLLM`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "manuelaschrittwieser/llama-3-invoice-extractor-merged"
+
+# Load Model and Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map="auto", 
+    torch_dtype=torch.float16
+)
+
+# Inference Example
+prompt = """### Instruction:
+Extract invoice details into JSON.
+
+### Input:
+Bought 2 monitors at Dell for 900 USD on Jan 15 2024.
+
+### Response:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:\n")[-1])
+
+```
+---
+
+Developed by: Manuela Schrittwieser,
+Project: Structured Data Extractor