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Model: vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - qwen
+  - llm
+  - sft
+  - conversational
+  - transformers
+  - pytorch
+---
+
+# Supervised Fine-Tuned Qwen2.5-0.5B-Instruct (SFT)
+
+This repository contains a **Supervised Fine-Tuned (SFT)** version of **Qwen2.5-0.5B-Instruct**. The model has been fine-tuned on a custom instruction-following conversational dataset to answer questions about Vishnu in a natural and helpful manner.
+
+## Model Details
+
+* **Base Model:** `Qwen/Qwen2.5-0.5B-Instruct`
+* **Training Method:** Supervised Fine-Tuning (SFT)
+* **Framework:** Hugging Face Transformers
+* **Task:** Conversational Text Generation
+
+---
+
+# Installation
+
+```bash
+pip install transformers accelerate torch
+```
+
+---
+
+# Loading the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+MODEL_ID = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+```
+
+---
+
+# Example Inference
+
+```python
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are Vishnu's personal AI assistant. "
+            "Answer questions about Vishnu using the provided information."
+        )
+    },
+    {
+        "role": "user",
+        "content": "Tell me about Vishnu."
+    }
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer(
+    text,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256
+)
+
+response = tokenizer.decode(
+    outputs[0][inputs.input_ids.shape[-1]:],
+    skip_special_tokens=True
+)
+
+print(response)
+```
+
+---
+
+# Gradio Demo
+
+```python
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+MODEL_ID = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+def chat(message, history):
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are Vishnu's personal AI assistant. "
+                "Answer questions about Vishnu using the provided information."
+            )
+        }
+    ]
+
+    for user, assistant in history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": assistant})
+
+    messages.append({"role": "user", "content": message})
+
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt"
+    ).to(model.device)
+
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256
+    )
+
+    response = tokenizer.decode(
+        outputs[0][inputs.input_ids.shape[-1]:],
+        skip_special_tokens=True
+    )
+
+    return response
+
+gr.ChatInterface(chat).launch()
+```
+
+---
+
+# Generation Parameters
+
+| Parameter      |                         Value |
+| -------------- | ----------------------------: |
+| max_new_tokens |                           256 |
+| device_map     |                          auto |
+| torch_dtype    | float16 (GPU) / float32 (CPU) |
+
+---
+
+# Repository Structure
+
+```
+config.json
+generation_config.json
+model.safetensors
+tokenizer.json
+tokenizer_config.json
+chat_template.jinja
+training_args.bin
+```
+
+---
+
+# Training Overview
+
+This model was fine-tuned using **Supervised Fine-Tuning (SFT)** on a custom instruction-response dataset.
+
+The training process included:
+
+* Instruction-response formatting using the Qwen chat template
+* Hugging Face Transformers
+* TRL SFTTrainer
+* PyTorch
+* Custom conversational dataset
+
+---
+
+# Citation
+
+If you use this model in your work, please cite this repository.
+
+```bibtex
+@misc{vishnu_qwen25_sft,
+  author = {Vishnu Amarapu},
+  title = {Supervised Fine-Tuned Qwen2.5-0.5B-Instruct},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft}}
+}
+```
+
+---
+
+# Acknowledgements
+
+* Alibaba Cloud Qwen Team for the base model.
+* Hugging Face Transformers.
+* TRL (Transformer Reinforcement Learning).
+* PyTorch.
+