YoussefElsafi/PlayerAI-1.2B

PlayerAI-1.2B is a fine-tuned conversational language model designed for immersive, human-like interaction in multiplayer social environments. The model focuses on generating realistic chat behavior in fast-paced, informal dialogue systems where identity ambiguity and conversational realism are primary objectives.

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Model Overview

• Base Model: LiquidAI/LFM2.5-1.2B-Instruct
• Parameters: ~1.2B
• Architecture: Decoder-only Transformer
• Training Type: Supervised fine-tuning (full model)
• Context Style: Multi-turn conversational sequences
• Primary Objective: Social realism in dialogue generation

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Intended Use

This model is intended for research and experimental use cases involving:

• Multiplayer conversational agents
• Social simulation environments
• NPC dialogue systems
• Human-like chat behavior modeling
• Interactive roleplay systems

It is not intended for:

• factual question answering
• structured instruction following
• safety-critical systems
• deterministic reasoning tasks

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Training Data

The model was trained on a large-scale collection of synthetically generated and curated conversational sequences designed to replicate natural human-like chat behavior in multiplayer-style environments.

The dataset emphasizes:

• informal conversation structure
• rapid topic switching
• multi-turn dialogue continuity
• noisy and unstructured chat patterns
• social interaction realism over factual accuracy

No personally identifiable or sensitive user-specific content is included. The dataset is constructed to simulate conversational behavior patterns rather than reflect real individual interactions.

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Chat Format

Training samples are serialized using a newline-based structured format (\n) representing conversation turns.

Format Structure

Each sample follows:

input: <conversation history>\nAI:
output: <next assistant response>

Conversation turns inside the input are structured as:

AI: <message>\nUser: <message>\nAI: <message>\nUser: <message>

The model is trained to predict the final output given the full conversational history.

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Example Interactions

Note: All the white-colored messages are fully generated by PlayerAI-1.2B.

Example 1 — Single Turn

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Example 2 — Short Conversation

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Example 3 — Extended Context Chain

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Example 4 — Nonsense Interaction

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Example 5 — Accusation and Denial

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Training Objective

The model is optimized to:

• maintain coherence across multi-turn dialogue
• generate short, informal responses
• adapt dynamically to conversational tone
• handle noisy and inconsistent chat structures
• simulate realistic multiplayer chat behavior

Loss is applied only on assistant outputs, while user/context tokens are treated as conditioning input.

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Inference

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "YoussefElsafi/PlayerAI-1.2B"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)

tokenizer.pad_token = tokenizer.eos_token

input_text = "User: wsp\nAI:"

inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=80,
        do_sample=True,
        temperature=0.8,
        top_p=0.9,
        use_cache=True
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Multi-Turn Inference Example

input_text = (
    "User: hi\\n"
    "AI: hello\\n"
    "User: how are you\\n"
    "AI: good u?\\n"
    "User: also good, what is 2+2?\n"
    "AI:"
)

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Behavior Characteristics

The model exhibits:

• informal conversational tone
• short and adaptive responses
• occasional ambiguity or inconsistency
• strong dependence on recent dialogue context
• variability in emotional and linguistic style

These properties are intentional and aligned with the social simulation objective.

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Limitations

• Not suitable for factual reasoning tasks
• May produce inconsistent outputs in long contexts
• Limited stability in structured instruction formats
• Not optimized for deterministic responses
• Can exhibit unpredictable conversational drift

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Ethical Considerations

This model is intended for research and simulation purposes. Developers should be aware that:

• outputs may appear human-like in social contexts
• behavior is optimized for realism, not correctness
• conversational ambiguity is an intentional feature

Appropriate safeguards should be applied depending on deployment context.

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Attribution

If you use PlayerAI in a project, attribution is appreciated but not required:

"Powered by PlayerAI"

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License

This project is licensed under the Apache 2.0 License.