Text Generation
Transformers
Safetensors
English
llama
code
text-generation-inference
Information Extraction
IE
Named Entity Recogniton
Event Extraction
Relation Extraction
LLaMA
Instructions to use HiTZ/Llama-3.1-GuideX-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/Llama-3.1-GuideX-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HiTZ/Llama-3.1-GuideX-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HiTZ/Llama-3.1-GuideX-8B") model = AutoModelForCausalLM.from_pretrained("HiTZ/Llama-3.1-GuideX-8B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HiTZ/Llama-3.1-GuideX-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HiTZ/Llama-3.1-GuideX-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HiTZ/Llama-3.1-GuideX-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HiTZ/Llama-3.1-GuideX-8B
- SGLang
How to use HiTZ/Llama-3.1-GuideX-8B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "HiTZ/Llama-3.1-GuideX-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HiTZ/Llama-3.1-GuideX-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "HiTZ/Llama-3.1-GuideX-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HiTZ/Llama-3.1-GuideX-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HiTZ/Llama-3.1-GuideX-8B with Docker Model Runner:
docker model run hf.co/HiTZ/Llama-3.1-GuideX-8B
oraingoz, gero gehituko ditut gauza gehio
#1
by neildlf - opened
README.md
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---
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license: apache-2.0
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datasets:
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- HiTZ/GuideX_pre-training_data
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- ACE05
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- bc5cdr
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- conll2003
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- ncbi_disease
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- conll2012_ontonotesv5
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- rams
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- tacred
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- wnut_17
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language:
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- en
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metrics:
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- f1
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base_model:
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- meta-llama/Llama-3.1-8B
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tags:
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- code
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- text-generation-inference
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- Information Extraction
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- IE
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- Named Entity Recogniton
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- Event Extraction
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- Relation Extraction
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- LLaMA
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---
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<p align=
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# Model Card for GuideX-8B
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<p align="center">
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<br>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64fee0a7a10455384ebba184/IEq02_qWzwxeHd6T-UOVG.png
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" style="height: 150px;">
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<h2 align="center">Guided Synthetic Data Generation for Zero-Shot Information Extraction</h2>
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<br>
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</p>
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**Llama-3.1-GuideX-8B** is an 8-billion-parameter language model fine-tuned for high-performance zero-shot Information Extraction (IE). The model is trained to follow detailed annotation guidelines provided as Python dataclasses, allowing it to adapt to new domains and schemas on the fly without requiring task-specific examples.
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This model achieves state-of-the-art performance on zero-shot Named Entity Recognition (NER) by first training on `GuideX`, a large-scale synthetic dataset with executable guidelines, and then fine-tuning on a collection of gold-standard IE datasets.
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- **Homepage:** [https://neilus03.github.io/guidex.com/](https://neilus03.github.io/guidex.com/)
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- **Paper:** [GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction](https://arxiv.org/abs/2506.00649)
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- **Code & Data:** The code and data for reproducing the GuideX methodology are available on the project homepage.
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## Model Description
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- **Developed by:** Neil De La Fuente, Oscar Sainz, Iker García-Ferrero, Eneko Agirre
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- **Institution:** HiTZ Basque Center for Language Technology - Ixa NLP Group, University of the Basque Country (UPV/EHU), Technical University of Munich (TUM)
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- **Model type:** Decoder-only Transformer (Text Generation)
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- **Language(s):** English
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- **License:** Llama 2 Community License
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- **Finetuned from model:** `meta-llama/Llama-3.1-8B`
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