Update README.md

README.md

@@ -1,3 +1,101 @@
----
-license: mit
----
+---
+license: apache-2.0
+datasets:
+- IAmSkyDra/HCMUT_FAQ
+language:
+- vi
+tags:
+- education
+widget:
+- text: Chào bạn
+  output:
+    text: >-
+      Chào bạn! Tôi là GemSUra-edu, một trợ lý AI được phát triển bởi Long
+      Nguyen.
+---
+## Introduction
+
+GemSUra-edu is a large language model fine-tuned on a dataset of FAQs from HCMUT, based on the pre-trained model [GemSUra 2B](https://huggingface.co/ura-hcmut/GemSUra-2B) developed by the URA research group at Ho Chi Minh City University of Technology (HCMUT).
+
+## Inference (with Unsloth for higher speed)
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="IAmSkyDra/GemSUra-edu",
+    max_seq_length=4096,
+    dtype=None,
+    load_in_4bit=True
+)
+
+FastLanguageModel.for_inference(model)
+
+query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
+
+while True:
+    query = input("Query: ")
+    if query.lower() == "exit":
+        break
+
+    query = query_template.format(query=query)
+    inputs = tokenizer(query, return_tensors="pt")
+
+    outputs = model.generate(**inputs, max_new_tokens=4096, use_cache=True)
+    generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    answer = generated_text[0].split("model\n")[1].strip()
+    print(answer)
+```
+
+## Inference (with Transformers)
+
+```python
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+pipeline_kwargs = {
+    "temperature": 0.1,
+    "max_new_tokens": 4096,
+    "do_sample": True
+}
+
+if __name__ == "__main__":
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(
+        "IAmSkyDra/GemSUra-edu",
+        device_map="auto"
+    )
+    model.eval()
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(
+        "IAmSkyDra/GemSUra-edu",
+        trust_remote_code=True
+    )
+
+    pipeline = transformers.pipeline(
+        model=model,
+        tokenizer=tokenizer,
+        return_full_text=False,
+        task='text-generation',
+        **pipeline_kwargs
+    )
+
+    query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
+
+    while True:
+        query = input("Query: ")
+        if query.lower() == "exit":
+            break
+
+        query = query_template.format(query=query)
+        answer = pipeline(query)[0]["generated_text"]
+        answer = answer.split("model\n")[1].strip()
+        print(answer)
+```
+
+## Notation
+
+If you want to quantize the model for deployment on local devices, it should be quantized to at least 8 bits.