Chat Model

A chat-enabled language model for conversational AI tasks.

Model Description

This model is designed for chat and conversational use cases. It can engage in multi-turn conversations while maintaining context.

How to Use

Direct Usage (Hugging Face Chat Widget)

Simply go to the model page and use the built-in chat interface at the top.

Programmatic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/your-model-name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Chat with the model
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello, how are you?"}
]

# Apply chat template
chat_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat_text, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

With Pipeline

from transformers import pipeline

chat = pipeline("text-generation", model="your-username/your-model-name")

messages = [
    {"role": "user", "content": "What is the capital of France?"}
]

response = chat(messages, max_new_tokens=100)
print(response)

Model Details

Model type: Large Language Model
Training data: [Add your training data info]
License: Apache 2.0

Intended Use

This model is intended for:

Conversational AI applications
Customer service chatbots
Educational assistants
General-purpose question answering

Limitations

Model may generate factually incorrect information
Responses are based on training data patterns
Not suitable for critical decision-making without human review

Training Procedure

[Add details about your training process]

Citation

@misc{yourmodel2024,
  title={Chat Model},
  author={Your Name},
  year={2024},
  howpublished={\url{https://huggingface.co/your-username/your-model-name}}
}

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