---
license: apache-2.0
language:
- en
base_model:
- LiquidAI/LFM2.5-1.2B-Instruct
pipeline_tag: text-generation
---
**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.

---

## 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  

---

## 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  

---

## 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.

---

## 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.


---

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

### Example 1 — Single Turn

![Example1](https://cdn-uploads.huggingface.co/production/uploads/66777fbec5ec2bdc9468394a/8Vy2qzwhZdNng9q6RiuqW.png)

---

### Example 2 — Short Conversation

![example2](https://cdn-uploads.huggingface.co/production/uploads/66777fbec5ec2bdc9468394a/skHTwTbqUyimZyTsbUzm1.png)

---

### Example 3 — Extended Context Chain

![example3](https://cdn-uploads.huggingface.co/production/uploads/66777fbec5ec2bdc9468394a/Teo6pX7ATkn4cuXmG7Y_g.png)

---

### Example 4 — Nonsense Interaction

![example4](https://cdn-uploads.huggingface.co/production/uploads/66777fbec5ec2bdc9468394a/jIrpridFKCR-ar5JvHvit.png)

---

### Example 5 — Accusation and Denial

![example5](https://cdn-uploads.huggingface.co/production/uploads/66777fbec5ec2bdc9468394a/4vmZkSyMaHdQWVIbaGqBL.png)

---

## 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.

---

## Inference

### Basic Usage

```python
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))
```

---

### Multi-Turn Inference Example

```python
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:"
)
```

---

## 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.

---

## 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

---

## 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.

---

## Attribution

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

"**Powered by PlayerAI**"

---

## License

This project is licensed under the Apache 2.0 License.