Update README.md

README.md

@@ -1,3 +1,132 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+**How to use the model**
+
+To use the model with `transformer` package, see the example below:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "Ihor/OpenBioLLM-Text2Graph-8B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|end_of_text|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+
+MESSAGES = [
+    {
+        "role": "system",
+        "content": (
+            "You are an advanced assistant trained to process biomedical text for Named Entity Recognition (NER) and Relation Extraction (RE). "
+            "Your task is to analyze user-provided text, identify all unique and contextually relevant entities, and infer directed relationships "
+            "between these entities based on the context. Ensure that all relations exist only between annotated entities. "
+            "Entities and relationships should be human-readable and natural, reflecting real-world concepts and connections. "
+            "Output the annotated data in JSON format, structured as follows:\n\n"
+            """{"entities": [{"id": 0, "text": "ner_string_0", "type": "ner_type_string_0"}, {"id": 1, "text": "ner_string_1", "type": "ner_type_string_1"}], "relations": [{"head": 0, "tail": 1, "type": "re_type_string_0"}]}"""
+            "\n\nEnsure that the output captures all significant entities and their directed relationships in a clear and concise manner."
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            'Here is a text input: "Subjects will receive a 100mL dose of IV saline every 6 hours for 24 hours. The first dose will be administered prior to anesthesia induction, approximately 30 minutes before skin incision. A total of 4 doses will be given." '
+            "Analyze this text, select and classify the entities, and extract their relationships as per your instructions."
+        ),
+    },
+]
+
+# Build prompt text
+chat_prompt = tokenizer.apply_chat_template(
+    MESSAGES, tokenize=False, add_generation_prompt=True
+)
+
+# Tokenize
+inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
+
+# Generate
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=3000,
+    do_sample=True,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    return_dict_in_generate=True
+)
+
+# Decode ONLY the new tokens (skip the prompt tokens)
+prompt_len = inputs["input_ids"].shape[-1]
+generated_ids = outputs.sequences[0][prompt_len:]
+response = tokenizer.decode(generated_ids, skip_special_tokens=True)
+print(response)
+```
+
+To use the model with `vllm` package, please refer to the example below:
+```python
+# !pip install vllm
+
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+MODEL_ID = "Ihor/OpenBioLLM-Text2Graph-8B"  
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
+tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|end_of_text|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
+
+llm = LLM(model=MODEL_ID)
+
+sampling_params = SamplingParams(
+    max_tokens=3000,          
+    n=1,
+    best_of=1,
+    presence_penalty=0.0,
+    frequency_penalty=0.0,
+    repetition_penalty=1.0,
+    temperature=0.0,
+    top_p=1.0,
+    top_k=-1,
+    min_p=0.0,
+    seed=42,
+)
+
+
+MESSAGES = [
+    {
+        "role": "system",
+        "content": (
+            "You are an advanced assistant trained to process biomedical text for Named Entity Recognition (NER) and Relation Extraction (RE). "
+            "Your task is to analyze user-provided text, identify all unique and contextually relevant entities, and infer directed relationships "
+            "between these entities based on the context. Ensure that all relations exist only between annotated entities. "
+            "Entities and relationships should be human-readable and natural, reflecting real-world concepts and connections. "
+            "Output the annotated data in JSON format, structured as follows:\n\n"
+            """{"entities": [{"id": 0, "text": "ner_string_0", "type": "ner_type_string_0"}, {"id": 1, "text": "ner_string_1", "type": "ner_type_string_1"}], "relations": [{"head": 0, "tail": 1, "type": "re_type_string_0"}]}"""
+            "\n\nEnsure that the output captures all significant entities and their directed relationships in a clear and concise manner."
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            'Here is a text input: "Subjects will receive a 100mL dose of IV saline every 6 hours for 24 hours. The first dose will be administered prior to anesthesia induction, approximately 30 minutes before skin incision. A total of 4 doses will be given." '
+            "Analyze this text, select and classify the entities, and extract their relationships as per your instructions."
+        ),
+    },
+]
+
+chat_prompt = tokenizer.apply_chat_template(
+    MESSAGES,
+    tokenize=False,
+    add_generation_prompt=True,
+    add_special_tokens=False, 
+)
+
+outputs = llm.generate([chat_prompt], sampling_params)
+response_text = outputs[0].outputs[0].text
+print(response_text)
+```