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vishnuamarapu
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Model Details
Model Provider
vishnuamarapu
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-0.5B-Instructbash
pip install transformers accelerate torchpython
from transformers import AutoTokenizer, AutoModelForCausalLMimport 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")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)python
import gradio as grfrom transformers import AutoTokenizer, AutoModelForCausalLMimport 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()| Parameter | Value |
|---|---|
| max_new_tokens | 256 |
| device_map | auto |
| torch_dtype | float16 (GPU) / float32 (CPU) |
text
config.jsongeneration_config.jsonmodel.safetensorstokenizer.jsontokenizer_config.jsonchat_template.jinjatraining_args.binThis model was fine-tuned using Supervised Fine-Tuning (SFT) on a custom instruction-response dataset.
The training process included:
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}}}from transformers import AutoTokenizer, AutoModelForCausalLMimport 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")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)import gradio as grfrom transformers import AutoTokenizer, AutoModelForCausalLMimport 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()config.jsongeneration_config.jsonmodel.safetensorstokenizer.jsontokenizer_config.jsonchat_template.jinjatraining_args.bin@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}}}