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Model: pat-jj/text2graph-llama-3.2-3b Source: Original Platform
2026-06-16 08:28:12 +08:00
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+---
+library_name: transformers
+license: llama3.2
+base_model: unsloth/Llama-3.2-3B-Instruct
+tags:
+- trl
+- sft
+- generated_from_trainer
+model-index:
+- name: text2graph-llama-3.2-3b
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# text2graph-llama-3.2-3b
+
+This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on a text2triple dataset curated by Sonnet-3.5.
+
+This model has much faster inference speed than our previous trained [T5-based model](https://huggingface.co/pat-jj/text2triple-flan-t5). Also, it performs better for longer (> 512 tokens) input.
+
+
+# Example Input:
+"William Gerald Standridge (November 27, 1953 – April 12, 2014) was an American stock car racing driver. He was a competitor in the NASCAR Winston Cup Series and Busch Series."
+
+# Example Output:
+(S> William gerald standridge| P> Nationality| O> American), 
+\
+(S> William gerald standridge| P> Occupation| O> Stock car racing driver), 
+\
+(S> William gerald standridge| P> Competitor| O> Busch series), 
+\
+(S> William gerald standridge| P> Competitor| O> Nascar winston cup series), 
+\
+(S> William gerald standridge| P> Birth date| O> November 27, 1953),
+\
+(S> William gerald standridge| P> Death date| O> April 12, 2014)
+
+# How to Use?
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "pat-jj/text2graph-llama-3.2-3b"
+
+def load_model_and_tokenizer():
+    # Load the model and tokenizer
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16,
+        device_map="auto"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # Set up chat template
+    tokenizer.chat_template = tokenizer.chat_template or "llama-3.1"
+    
+    return model, tokenizer
+
+def generate_triples(model, tokenizer, input_text, max_length=2048):
+    # Format the input using chat template
+    messages = [{
+        "role": "user",
+        "content": f"Convert the following text to triples:\n\nText: {input_text}"
+    }]
+    
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    # Tokenize input
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate response
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id
+    )
+    
+    # Decode and return the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+def main():
+    print("Loading model and tokenizer...")
+    model, tokenizer = load_model_and_tokenizer()
+    
+    print("\nModel loaded! Enter text to convert to triples (type 'quit' to exit):")
+    
+    while True:
+        user_input = input("\nEnter text: ")
+        if user_input.lower() == 'quit':
+            break
+            
+        print("\nGenerating triples...")
+        response = generate_triples(model, tokenizer, user_input)
+        print("\nResponse:", response)
+
+if __name__ == "__main__":
+    main()
+```
+
+
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 3
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 3
+- gradient_accumulation_steps: 3
+- total_train_batch_size: 27
+- total_eval_batch_size: 3
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Framework versions
+
+- Transformers 4.48.1
+- Pytorch 2.1.2+cu121
+- Datasets 2.21.0
+- Tokenizers 0.21.0
+
+
+## **Cite Our [Paper](https://arxiv.org/abs/2502.10996)**
+```
+@misc{jiang2025rasretrievalandstructuringknowledgeintensivellm,
+      title={RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation}, 
+      author={Pengcheng Jiang and Lang Cao and Ruike Zhu and Minhao Jiang and Yunyi Zhang and Jimeng Sun and Jiawei Han},
+      year={2025},
+      eprint={2502.10996},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.10996}, 
+}
+```