Text Generation
Transformers
Safetensors
English
gpt2
conversational
chatbot
romantic
supportive
fine-tuned
text-generation-inference
Instructions to use sambitsingha/mrbully-chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sambitsingha/mrbully-chatbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sambitsingha/mrbully-chatbot") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sambitsingha/mrbully-chatbot") model = AutoModelForCausalLM.from_pretrained("sambitsingha/mrbully-chatbot", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sambitsingha/mrbully-chatbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sambitsingha/mrbully-chatbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sambitsingha/mrbully-chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sambitsingha/mrbully-chatbot
- SGLang
How to use sambitsingha/mrbully-chatbot with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "sambitsingha/mrbully-chatbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sambitsingha/mrbully-chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "sambitsingha/mrbully-chatbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sambitsingha/mrbully-chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sambitsingha/mrbully-chatbot with Docker Model Runner:
docker model run hf.co/sambitsingha/mrbully-chatbot
Add model card
Browse files
README.md
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---
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language: en
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license: mit
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tags:
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- conversational
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- chatbot
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- fine-tuned
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datasets:
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- custom
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---
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# Mr.Bully Chatbot Model
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This is a fine-tuned conversational AI model trained on custom dialogue data.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("sambitsingha/mrbully-chatbot")
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model = AutoModelForCausalLM.from_pretrained("sambitsingha/mrbully-chatbot")
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# Generate response
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prompt = "User: Hello\nMr.Bully:"
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(inputs, max_length=150, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Model Details
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- Base model: GPT-2 (or specify your base model)
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- Fine-tuned on custom conversational data
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- Optimized for romantic/affectionate conversations
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