This model leverages the Llama 3 and fine-tunes it on SimsChat, a high-quality, simulated role-playing dialogue dataset. The goal is to create a chat model that excels at daily conversation, emotional expression, and role-playing.

The SimsConv dataset is designed according to "The Sims 4" game, containing a large volume of simulated daily conversation scenarios. As a result, this fine-tuned model may perform better in:

Colloquial & Natural Language: Dialogue style is closer to everyday speech rather than formal written language.
Emotional Awareness: The training data includes emotional labels, making the model more sensitive to emotional context.
Role-playing: The model is better suited to simulate specific characters or personas (similar to "Sims" in the game).

Uses

Intended Uses

This model is intended for conversational scenarios, including:

Chatbot: A general-purpose chatbot for fluent, everyday conversation.
Role-playing: Simulating specific characters (especially Sims-like personas) for interaction.
Creative Writing: Assisting in generating dialogue scripts or stories.

How to Use

You will need to install transformers (v4.40.0 or higher recommended) and torch.

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# -------------------------------------------------------------------
# Replace with your model ID (e.g., "yangbh217/SimsChat-Llama-3-8B")
# -------------------------------------------------------------------
model_id = "your-username/model_name"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16, # Recommended for VRAM savings
device_map="auto", # Automatically maps to GPU
)

# --- Method 1: Use Transformers Pipeline (Recommended) ---
print("--- Pipeline Example ---")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)

# Dialogue format required by Llama-3 chat template
messages = [
{"role": "system", "content": "You are an AI assistant, role-playing as a character from 'The Sims'."},
{"role": "user", "content": "Hi! How are you feeling today?"},
]

# Specific terminators for Llama-3
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipe(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.7,
top_p=0.9,
)

# outputs[0]["generated_text"] contains the full conversation history
# We only print the assistant's last response
print(outputs[0]["generated_text"][-1]['content'])


# --- Method 2: Manually Apply Chat Template ---
print("\n--- Manual Template Example ---")

# Manually apply the Llama-3 chat template
# add_generation_prompt=True automatically adds <|start_header_id|>assistant<|end_header_id|>\n\n
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)

# Encode
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate
outputs_manual = model.generate(
**inputs,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.7,
top_p=0.9,
)

# Decode (only the newly generated part)
response_ids = outputs_manual[0][inputs.input_ids.shape[1]:]
response = tokenizer.decode(response_ids, skip_special_tokens=True)
print(response)