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Model: fairdataihub/Llama-3.1-8B-Poster-Extraction
Source: Original Platform

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README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+  - poster-extraction
+  - scientific-posters
+  - machine-actionable
+  - FAIR-data
+  - biomedical
+  - document-understanding
+  - structured-extraction
+  - datacite
+  - text-generation
+  - llama3
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Llama-3.1-8B-Poster-Extraction
+
+## Model Description
+
+This model powers the extraction pipeline for [**posters.science**](https://posters.science), a platform for making scientific conference posters Findable, Accessible, Interoperable, and Reusable (FAIR).
+
+The model converts raw poster text into structured JSON metadata conforming to the [**poster-json-schema**](https://github.com/fairdataihub/poster-json-schema)—a DataCite-based schema extended for poster-specific metadata including conference information, content sections, and figure/table captions.
+
+Developed by the [**FAIR Data Innovations Hub**](https://fairdataihub.org/) at the California Medical Innovations Institute (CalMI²).
+
+## poster2json Library
+
+This model is the core of the **poster2json** Python library:
+
+| Resource | Link |
+|----------|------|
+| **PyPI** | [poster2json](https://pypi.org/project/poster2json/) |
+| **Documentation** | [fairdataihub.github.io/poster2json](https://fairdataihub.github.io/poster2json/) |
+| **GitHub** | [fairdataihub/poster2json](https://github.com/fairdataihub/poster2json) |
+| **API Repository** | [fairdataihub/posters-science-extraction-api](https://github.com/fairdataihub/posters-science-extraction-api) |
+| **Platform** | [posters.science](https://posters.science) |
+
+### Quick Install
+
+```bash
+pip install poster2json
+```
+
+### Python Usage
+
+```python
+from poster2json import extract_poster
+
+result = extract_poster("path/to/poster.pdf")
+print(result["titles"][0]["title"])
+print(result["creators"])
+```
+
+## Output Schema
+
+Output conforms to the [poster-json-schema](https://github.com/fairdataihub/poster-json-schema), based on [DataCite Metadata Schema](https://datacite.org/) with poster-specific extensions:
+
+```json
+{
+  "$schema": "https://posters.science/schema/v0.1/poster_schema.json",
+  "creators": [
+    {
+      "name": "Garcia, Sofia",
+      "givenName": "Sofia",
+      "familyName": "Garcia",
+      "nameType": "Personal",
+      "affiliation": ["University of California, San Diego"]
+    }
+  ],
+  "titles": [
+    { "title": "Machine Learning Approaches to Diabetic Retinopathy Detection" }
+  ],
+  "publicationYear": 2025,
+  "subjects": [
+    { "subject": "Machine Learning" },
+    { "subject": "Diabetic Retinopathy" }
+  ],
+  "descriptions": [
+    {
+      "description": "This poster presents machine learning methods for automated diabetic retinopathy screening...",
+      "descriptionType": "Abstract"
+    }
+  ],
+  "conference": {
+    "conferenceName": "AMIA 2025 Annual Symposium",
+    "conferenceLocation": "San Francisco, CA"
+  },
+  "content": {
+    "sections": [
+      { "sectionTitle": "Introduction", "sectionContent": "..." },
+      { "sectionTitle": "Methods", "sectionContent": "..." },
+      { "sectionTitle": "Results", "sectionContent": "..." },
+      { "sectionTitle": "Conclusions", "sectionContent": "..." }
+    ]
+  },
+  "imageCaptions": [
+    { "caption": "Figure 1. ROC curves showing model performance across datasets" }
+  ],
+  "tableCaptions": [
+    { "caption": "Table 1. Summary of demographic characteristics" }
+  ],
+  "rightsList": [
+    { "rights": "Creative Commons Attribution 4.0 International" }
+  ],
+  "formats": ["PDF"]
+}
+```
+
+### Key Schema Fields (DataCite-based)
+
+| Field | Description |
+|-------|-------------|
+| `creators` | Authors with name, affiliation, ORCID identifiers |
+| `titles` | Main title and alternative/translated titles |
+| `subjects` | Keywords and classification codes (MeSH, LCSH) |
+| `descriptions` | Abstract, methods, technical information |
+| `conference` | Conference name, location, dates, URI |
+| `content.sections` | Extracted poster sections with titles and content |
+| `imageCaptions` | Figure captions extracted from the poster |
+| `tableCaptions` | Table captions extracted from the poster |
+| `fundingReferences` | Grant information (funder, award number) |
+| `rightsList` | License information (CC-BY, etc.) |
+| `relatedIdentifiers` | DOIs, URLs to related resources |
+
+## Model Specifications
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | meta-llama/Llama-3.1-8B-Instruct |
+| Parameters | 8 Billion |
+| Context Length | 128K tokens |
+| Architecture | LLaMA 3.1 |
+| Precision | bfloat16 |
+| License | Llama 3.1 Community License |
+
+## Performance
+
+Validated on 10 manually annotated scientific posters:
+
+| Metric | Score | Threshold |
+|--------|-------|-----------|
+| Word Capture | 0.96 | ≥0.75 |
+| ROUGE-L | 0.89 | ≥0.75 |
+| Number Capture | 0.93 | ≥0.75 |
+| Field Proportion | 0.99 | 0.50–2.00 |
+
+**Pass Rate**: 10/10 (100%)
+
+## Direct Usage (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "fairdataihub/Llama-3.1-8B-Poster-Extraction"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+prompt = """Extract structured metadata from the following scientific poster.
+Return valid JSON conforming to the poster-json-schema with fields:
+creators, titles, publicationYear, subjects, descriptions, conference, content, imageCaptions, tableCaptions.
+
+Poster Content:
+[Your poster text here]
+"""
+
+messages = [{"role": "user", "content": prompt}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.1)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## System Requirements
+
+- **GPU**: NVIDIA CUDA-capable, ≥16GB VRAM (RTX 4090 recommended)
+- **RAM**: ≥32GB
+- Supports 8-bit quantization for memory-constrained environments
+- Compatible with vLLM and other inference optimization frameworks
+
+## Citation
+
+```bibtex
+@software{poster2json2026,
+  title = {poster2json: Scientific Poster to JSON Metadata Extraction},
+  author = {O'Neill, James and Soundarajan, Sanjay and Portillo, Dorian and Patel, Bhavesh},
+  year = {2026},
+  url = {https://github.com/fairdataihub/poster2json},
+  doi = {10.5281/zenodo.18320010}
+}
+```
+
+## License
+
+This model is released under the [Llama 3.1 Community License](https://ai.meta.com/llama/license/).
+
+## Acknowledgments
+
+- [FAIR Data Innovations Hub](https://fairdataihub.org/) at California Medical Innovations Institute (CalMI²)
+- [posters.science](https://posters.science) platform
+- Meta AI for the Llama 3.1 base model
+- HuggingFace for model hosting infrastructure
+- Funded by The Navigation Fund ([10.71707/rk36-9x79](https://doi.org/10.71707/rk36-9x79)) — "Poster Sharing and Discovery Made Easy"

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
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+    128008,
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+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "low_freq_factor": 1.0,
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+    "rope_type": "llama3"
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+  "do_sample": true,
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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.42.3"
+}

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