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Model: fairdataihub/Llama-3.1-8B-Poster-Extraction Source: Original Platform
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---
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- poster-extraction
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- scientific-posters
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- machine-actionable
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- FAIR-data
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- biomedical
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- document-understanding
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- structured-extraction
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- datacite
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- text-generation
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- llama3
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Llama-3.1-8B-Poster-Extraction
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## Model Description
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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).
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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.
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Developed by the [**FAIR Data Innovations Hub**](https://fairdataihub.org/) at the California Medical Innovations Institute (CalMI²).
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## poster2json Library
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This model is the core of the **poster2json** Python library:
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| Resource | Link |
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|----------|------|
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| **PyPI** | [poster2json](https://pypi.org/project/poster2json/) |
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| **Documentation** | [fairdataihub.github.io/poster2json](https://fairdataihub.github.io/poster2json/) |
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| **GitHub** | [fairdataihub/poster2json](https://github.com/fairdataihub/poster2json) |
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| **API Repository** | [fairdataihub/posters-science-extraction-api](https://github.com/fairdataihub/posters-science-extraction-api) |
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| **Platform** | [posters.science](https://posters.science) |
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### Quick Install
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```bash
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pip install poster2json
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```
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### Python Usage
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```python
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from poster2json import extract_poster
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result = extract_poster("path/to/poster.pdf")
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print(result["titles"][0]["title"])
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print(result["creators"])
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```
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## Output Schema
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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:
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```json
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{
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"$schema": "https://posters.science/schema/v0.1/poster_schema.json",
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"creators": [
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{
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"name": "Garcia, Sofia",
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"givenName": "Sofia",
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"familyName": "Garcia",
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"nameType": "Personal",
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"affiliation": ["University of California, San Diego"]
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}
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],
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"titles": [
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{ "title": "Machine Learning Approaches to Diabetic Retinopathy Detection" }
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],
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"publicationYear": 2025,
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"subjects": [
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{ "subject": "Machine Learning" },
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{ "subject": "Diabetic Retinopathy" }
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],
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"descriptions": [
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{
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"description": "This poster presents machine learning methods for automated diabetic retinopathy screening...",
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"descriptionType": "Abstract"
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}
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],
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"conference": {
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"conferenceName": "AMIA 2025 Annual Symposium",
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"conferenceLocation": "San Francisco, CA"
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},
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"content": {
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"sections": [
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{ "sectionTitle": "Introduction", "sectionContent": "..." },
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{ "sectionTitle": "Methods", "sectionContent": "..." },
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{ "sectionTitle": "Results", "sectionContent": "..." },
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{ "sectionTitle": "Conclusions", "sectionContent": "..." }
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]
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},
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"imageCaptions": [
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{ "caption": "Figure 1. ROC curves showing model performance across datasets" }
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],
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"tableCaptions": [
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{ "caption": "Table 1. Summary of demographic characteristics" }
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],
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"rightsList": [
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{ "rights": "Creative Commons Attribution 4.0 International" }
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],
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"formats": ["PDF"]
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}
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```
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### Key Schema Fields (DataCite-based)
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| Field | Description |
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|-------|-------------|
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| `creators` | Authors with name, affiliation, ORCID identifiers |
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| `titles` | Main title and alternative/translated titles |
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| `subjects` | Keywords and classification codes (MeSH, LCSH) |
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| `descriptions` | Abstract, methods, technical information |
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| `conference` | Conference name, location, dates, URI |
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| `content.sections` | Extracted poster sections with titles and content |
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| `imageCaptions` | Figure captions extracted from the poster |
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| `tableCaptions` | Table captions extracted from the poster |
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| `fundingReferences` | Grant information (funder, award number) |
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| `rightsList` | License information (CC-BY, etc.) |
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| `relatedIdentifiers` | DOIs, URLs to related resources |
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## Model Specifications
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| Attribute | Value |
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|-----------|-------|
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| Base Model | meta-llama/Llama-3.1-8B-Instruct |
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| Parameters | 8 Billion |
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| Context Length | 128K tokens |
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| Architecture | LLaMA 3.1 |
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| Precision | bfloat16 |
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| License | Llama 3.1 Community License |
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## Performance
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Validated on 10 manually annotated scientific posters:
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| Metric | Score | Threshold |
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|--------|-------|-----------|
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| Word Capture | 0.96 | ≥0.75 |
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| ROUGE-L | 0.89 | ≥0.75 |
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| Number Capture | 0.93 | ≥0.75 |
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| Field Proportion | 0.99 | 0.50–2.00 |
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**Pass Rate**: 10/10 (100%)
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## Direct Usage (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "fairdataihub/Llama-3.1-8B-Poster-Extraction"
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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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torch_dtype="auto",
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device_map="auto"
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)
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prompt = """Extract structured metadata from the following scientific poster.
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Return valid JSON conforming to the poster-json-schema with fields:
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creators, titles, publicationYear, subjects, descriptions, conference, content, imageCaptions, tableCaptions.
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Poster Content:
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[Your poster text here]
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"""
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messages = [{"role": "user", "content": prompt}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.1)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## System Requirements
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- **GPU**: NVIDIA CUDA-capable, ≥16GB VRAM (RTX 4090 recommended)
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- **RAM**: ≥32GB
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- Supports 8-bit quantization for memory-constrained environments
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- Compatible with vLLM and other inference optimization frameworks
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## Citation
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```bibtex
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@software{poster2json2026,
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title = {poster2json: Scientific Poster to JSON Metadata Extraction},
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author = {O'Neill, James and Soundarajan, Sanjay and Portillo, Dorian and Patel, Bhavesh},
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year = {2026},
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url = {https://github.com/fairdataihub/poster2json},
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doi = {10.5281/zenodo.18320010}
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}
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```
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## License
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This model is released under the [Llama 3.1 Community License](https://ai.meta.com/llama/license/).
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## Acknowledgments
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- [FAIR Data Innovations Hub](https://fairdataihub.org/) at California Medical Innovations Institute (CalMI²)
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- [posters.science](https://posters.science) platform
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- Meta AI for the Llama 3.1 base model
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- HuggingFace for model hosting infrastructure
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- Funded by The Navigation Fund ([10.71707/rk36-9x79](https://doi.org/10.71707/rk36-9x79)) — "Poster Sharing and Discovery Made Easy"
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config.json
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"LlamaForCausalLM"
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"hidden_size": 4096,
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"initializer_range": 0.02,
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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_key_value_heads": 8,
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"tie_word_embeddings": false,
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"transformers_version": "4.42.3",
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"use_cache": true,
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"vocab_size": 128256
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}
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||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user