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Model: manuelaschrittwieser/llama-3-invoice-extractor-merged Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model: meta-llama/Meta-Llama-3-8B
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library_name: unsloth
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tags:
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- finance
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- information-extraction
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- structured-data
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- json
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- merged
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- llama-3
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datasets:
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- manuelaschrittwieser/invoice-extraction-dataset-v2
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metrics:
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- train_loss
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model-index:
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- name: Llama-3-8B Invoice Extractor (Merged)
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results:
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- task:
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type: text-generation
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name: Information Extraction
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dataset:
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name: invoice-extraction-dataset-v2
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type: manuelaschrittwieser/invoice-extraction-dataset-v2
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metrics:
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- name: Final Train Loss
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type: loss
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value: 0.2555
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pipeline_tag: text-generation
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finetuned_from: meta-llama/Meta-Llama-3-8B
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---
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# 📑 Llama-3-8B Invoice Extractor (Merged)
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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## 1. Model Description & Intended Use
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This model is a fine-tuned version of **Meta's Llama-3-8B**, specifically optimized for **Structured Information Extraction**.
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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.
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**Primary Intended Use:**
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- Automated accounting and bookkeeping workflows.
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- Extracting data from OCR-processed receipts.
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- Building serverless financial data pipelines.
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**Target Schema:**
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```json
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{
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"item": "string",
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"quantity": "integer",
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"date": "string",
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"vendor": "string",
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"total": "float",
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"currency": "string"
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}
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```
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## 2. Training Data Information
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The model was trained on the `manuelaschrittwieser/invoice-extraction-dataset-v2`, which consists of **601 high-quality examples**.
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* **Methodology:** The dataset uses a synthetic generation pipeline with 7 distinct sentence templates to ensure robustness against different linguistic structures.
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* **Diversity:** Includes 20+ vendors, 20+ item categories, and 5 currency types (USD, EUR, GBP, CAD, JPY).
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* **Format:** The data was prepared in an instruction-input-output format to reinforce strict adherence to the JSON schema.
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## 3. Training Procedure & Hyperparameters
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The training was conducted using **Parameter-Efficient Fine-Tuning (PEFT)** with the **QLoRA** method via the **Unsloth** library,
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which significantly reduced VRAM usage while maintaining performance.
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### Hyperparameters:
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* **Epochs:** Custom step-based (120 global steps)
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* **Learning Rate:** 2e-4
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* **Batch Size:** 2 (with Gradient Accumulation Steps = 4)
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* **Optimizer:** AdamW (8-bit)
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* **Learning Rate Scheduler:** Linear
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* **LoRA Rank (r):** 16
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* **LoRA Alpha:** 16
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* **Precision:** 4-bit Quantization (merged into 16-bit for this standalone version)
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## 4. Evaluation Results
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### Baseline (Llama-3-8B) vs. Fine-Tuned
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| **Feature** | **Baseline Model** | **Fine-Tuned Model (v2)** |
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| ------------- |:-------------:| :-------------:|
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| **Output Format** | Conversational / Explanatory | **Strict JSON Only** |
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| **JSON Validity** | Often fails (includes extra text) | **100% Valid JSON** in test runs |
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| **Entity Recognition** | High accuracy, but low precision | **High Precision** (mapped to schema) |
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| **Instruction Following** | Moderate | **High** (Stays within Response block) |
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**Tracking:** Training performance was monitored via **Weights & Biases (W&B)**, showing a consistent reduction in loss over 120 steps without signs of overfitting.
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## 5. Limitations & Known Issues
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* **Language:** Optimized primarily for English. Performance on other languages is not guaranteed.
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* **Hallucination:** While the model is highly structured, it can occasionally misinterpret dates if they are provided in ambiguous formats (e.g., 01/02/03).
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* **Context Length:** Best performance is achieved with input lengths under 512 tokens.
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* **Verification:** Users should implement a JSON validation layer in production to handle the rare cases of malformed output.
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## 6. Code Example: Loading and Usage
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Since this is a merged model, it can be used with standard `transformers` or `vLLM`.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "manuelaschrittwieser/llama-3-invoice-extractor-merged"
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# Load Model and Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.float16
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)
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# Inference Example
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prompt = """### Instruction:
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Extract invoice details into JSON.
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### Input:
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Bought 2 monitors at Dell for 900 USD on Jan 15 2024.
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### Response:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:\n")[-1])
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```
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---
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Developed by: Manuela Schrittwieser,
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Project: Structured Data Extractor
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"torch_dtype": "float16",
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128255,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"unsloth_version": "2026.2.1",
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"use_cache": true,
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"vocab_size": 128256
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}
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"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|reserved_special_token_250|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
|
||||
size 17209961
|
||||
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user