Instructions to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF",
	filename="PlayerAI-1.2B-v1.5-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Ollama
How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Ollama:
```
ollama run hf.co/YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
```

Unsloth Studio

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF to start chatting

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Docker Model Runner:
```
docker model run hf.co/YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M
```

Lemonade

How to use YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PlayerAI-1.2B-v1.5-GGUF-Q4_K_M

List all available models

lemonade list

PlayerAI-1.2B-v1.5-GGUF contains GGUF quantized versions of PlayerAI-1.2B-v1.5, a fine-tuned conversational language model designed for immersive, human-like interaction in multiplayer social environments.

This version improves conversational coherence, tone stability, and multi-turn consistency compared to previous releases, while remaining optimized for lightweight local inference.

Available Quantizations

File	Quant	Size	Quality	Recommended For
`PlayerAI-1.2B-v1.5-Q2_K.gguf`	Q2_K	483 MB	Lowest	Very limited RAM
`PlayerAI-1.2B-v1.5-Q3_K_S.gguf`	Q3_K_S	558 MB	Very Low	Minimal RAM
`PlayerAI-1.2B-v1.5-Q3_K_M.gguf`	Q3_K_M	600 MB	Low	Low RAM
`PlayerAI-1.2B-v1.5-Q3_K_L.gguf`	Q3_K_L	635 MB	Low-Med	Low RAM
`PlayerAI-1.2B-v1.5-iQ4_XS.gguf`	IQ4_XS	669 MB	Medium	Better than Q4 at same size
`PlayerAI-1.2B-v1.5-iQ4_NL.gguf`	IQ4_NL	700 MB	Medium	Better than Q4 at same size
`PlayerAI-1.2B-v1.5-Q4_K_S.gguf`	Q4_K_S	700 MB	Medium	Balanced
`PlayerAI-1.2B-v1.5-Q4_K_M.gguf`	Q4_K_M	731 MB	Medium	⭐ Recommended
`PlayerAI-1.2B-v1.5-Q5_K_S.gguf`	Q5_K_S	825 MB	Good	High quality
`PlayerAI-1.2B-v1.5-Q5_K_M.gguf`	Q5_K_M	843 MB	Good	High quality
`PlayerAI-1.2B-v1.5-Q6_K.gguf`	Q6_K	963 MB	High	Near lossless
`PlayerAI-1.2B-v1.5-Q8_0.gguf`	Q8_0	1.25 GB	Very High	Best quality
`PlayerAI-1.2B-v1.5-BF16.gguf`	BF16	2.34 GB	Native precision	Reference
`PlayerAI-1.2B-v1.5-F16.gguf`	F16	2.34 GB	Full	Reference / conversion

Which One Should I Pick?

Since this is a 1.2B model, all quantizations are lightweight enough for local use.


Any device with 1GB+ RAM → Q4_K_M ⭐ (recommended)
Best quality            → Q8_0
Lowest size             → Q2_K
Balanced performance    → Q3_K_M / Q4_K_S
No limits               → F16 / BF16

How to Use

With llama.cpp CLI

hf download YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF \
    PlayerAI-1.2B-v1.5-Q4_K_M.gguf \
    --local-dir ./PlayerAI-GGUF

./llama.cpp/build/bin/llama-cli \
    -m ./PlayerAI-GGUF/PlayerAI-1.2B-v1.5-Q4_K_M.gguf \
    -p "User: hi\nAI:" \
    -n 100 \
    --temp 0.8 \
    --top-p 0.9

With llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF",
    filename="PlayerAI-1.2B-v1.5-Q4_K_M.gguf",
    n_ctx=512,
    verbose=False,
)

SYSTEM_PROMPT = (
    "You are a human-like player in a multiplayer chat environment. "
    "Respond casually, with short informal messages and natural tone."
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": "hi wsp"},
    ],
    max_tokens=80,
    temperature=0.8,
    top_p=0.9,
)

print(response["choices"][0]["message"]["content"])

Model Overview

Base Model: LiquidAI/LFM2.5-1.2B-Instruct
Parent Model: PlayerAI-1.2B-v1.5
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

Example Interactions

Note: All assistant messages are generated by PlayerAI-1.2B-v1.5.

Example 1 — Single Turn

Example 2 — Short Conversation

Example 3 — Extended Context Chain

Example 4 — Nonsense Interaction

Example 5 — Reverse psychology

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 conversational drift

Ethical Considerations

This model is intended for research and simulation purposes.

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 (Optional)

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

"Powered by PlayerAI"

License

Apache 2.0

Downloads last month: 168

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for YoussefElsafi/PlayerAI-1.2B-v1.5-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(59)

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