Instructions to use KookiesXy/Neo50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KookiesXy/Neo50M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="KookiesXy/Neo50M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("KookiesXy/Neo50M")
model = AutoModelForMultimodalLM.from_pretrained("KookiesXy/Neo50M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use KookiesXy/Neo50M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KookiesXy/Neo50M",
	filename="gguf/neo50m-f16.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 KookiesXy/Neo50M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KookiesXy/Neo50M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf KookiesXy/Neo50M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KookiesXy/Neo50M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf KookiesXy/Neo50M: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 KookiesXy/Neo50M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KookiesXy/Neo50M: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 KookiesXy/Neo50M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KookiesXy/Neo50M:Q4_K_M

Use Docker

docker model run hf.co/KookiesXy/Neo50M:Q4_K_M

LM Studio
Jan

vLLM

How to use KookiesXy/Neo50M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KookiesXy/Neo50M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KookiesXy/Neo50M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KookiesXy/Neo50M:Q4_K_M

SGLang

How to use KookiesXy/Neo50M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "KookiesXy/Neo50M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KookiesXy/Neo50M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "KookiesXy/Neo50M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KookiesXy/Neo50M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use KookiesXy/Neo50M with Ollama:
```
ollama run hf.co/KookiesXy/Neo50M:Q4_K_M
```

Unsloth Studio

How to use KookiesXy/Neo50M 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 KookiesXy/Neo50M 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 KookiesXy/Neo50M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KookiesXy/Neo50M to start chatting

Atomic Chat new
Docker Model Runner
How to use KookiesXy/Neo50M with Docker Model Runner:
```
docker model run hf.co/KookiesXy/Neo50M:Q4_K_M
```

Lemonade

How to use KookiesXy/Neo50M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KookiesXy/Neo50M:Q4_K_M

Run and chat with the model

lemonade run user.Neo50M-Q4_K_M

List all available models

lemonade list

Neo50M

Neo50M is a tiny decoder-only chat language model trained from scratch. It is designed for toy/local assistant use, educational experiments, lightweight generation, and testing training pipelines.

Model Details

Type: decoder-only causal language model, Llama-compatible architecture
Parameters: approximately 52.6M
Context length target: 16k tokens
Training target: about 15B pretraining tokens plus chat/instruction tuning
Hardware: 8x NVIDIA RTX 5090 cloud GPUs
Tokenizer: TinyLlama/Llama-style 32k tokenizer with a Neo50M chat template

Intended Uses

toy/local assistant experiments
educational training and inference demos
lightweight generation
testing HF, GGUF, ONNX, and distributed training pipelines

Limitations

Neo50M is very small. It is not reliable for factual accuracy, has limited reasoning ability, may hallucinate, and should not be used for safety-critical decisions or high-stakes advice.

Transformers Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "KookiesXy/Neo50M"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, device_map="auto")

messages = [{"role": "user", "content": "Write a short thank-you note."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=120, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

GGUF Usage

After downloading a GGUF file:

llama-cli -m neo50m-q4_k_m.gguf -p "User: Write a haiku about GPUs.\nAssistant:"

ONNX Usage

The ONNX export is intended for forward-pass validation and integration experiments. Use ONNX Runtime to load onnx/model.onnx and feed integer input_ids plus attention_mask.

Dataset Summary

The training pipeline streams a configurable mixture of FineWeb-Edu, Cosmopedia, Wikipedia-like text, TinyStories, and a small permissive code component. SFT uses OpenHermes-style, UltraChat-style, Alpaca-style, and small refusal/helpfulness examples when available. Dataset availability can change; the exact configs are included with the upload.

Eval Results

Eval artifacts, when present, are uploaded under evals/.

Downloads last month: 89

Safetensors

Model size

52.6M params

Tensor type

F32