Instructions to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with Transformers:

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

pipe = pipeline("text-generation", model="KrynexLabs/KrynexAI-1.4-150M-Mobile-Model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KrynexLabs/KrynexAI-1.4-150M-Mobile-Model")
model = AutoModelForCausalLM.from_pretrained("KrynexLabs/KrynexAI-1.4-150M-Mobile-Model", device_map="auto")

Transformers.js

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'KrynexLabs/KrynexAI-1.4-150M-Mobile-Model');

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

Use Docker

docker model run hf.co/KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

SGLang

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model 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 "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model" \
    --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": "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model",
		"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 "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model" \
        --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": "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with Ollama:
```
ollama run hf.co/KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with Docker Model Runner:
```
docker model run hf.co/KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M
```

Lemonade

How to use KrynexLabs/KrynexAI-1.4-150M-Mobile-Model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KrynexLabs/KrynexAI-1.4-150M-Mobile-Model:Q4_K_M

Run and chat with the model

lemonade run user.KrynexAI-1.4-150M-Mobile-Model-Q4_K_M

List all available models

lemonade list

Atomic Chat

KrynexAI 1.4

KrynexAI 1.4 is a compact, ultra-lightweight 150M-parameter language model designed for high-efficiency text generation, fast inference, and on-device deployment.

🧠 Model Summary

KrynexAI 1.4 features an optimized Llama-based architecture trained on a multi-trillion token mix of web content, code, and educational data. It provides strong base capabilities in reasoning, instruction following, and general text processing while maintaining a minimal memory footprint (~270 MB weights).

Architecture: Llama-based Transformer Decoder
Parameters: ~150M
Context Window: 8,192 tokens
Precision: bfloat16 / fp16

🚀 Quickstart & Usage

pip install transformers

🐍 Basic Inference (Python)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "KrynexLabs/KrynexAI-1.4-150M-Mobile-Model"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(
    checkpoint, 
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📌 Performance Benchmarks

Metric	KrynexAI 1.4
HellaSwag	42.1
ARC (Average)	43.9
PIQA	68.4
MMLU (cloze)	31.5
CommonsenseQA	33.9
OpenBookQA	34.6

Warning: This repository may contain inaccurate, outdated, or otherwise unsuitable files. Approximately half of the original files were left unchanged, so their contents may not reflect the current state of the project or may no longer be relevant.

🎥 Full Video Review: Tests & Failures

Watch a full 44-minute independent review covering generation tests, prompt handling, and honest breakdowns of where this 150M model succeeds and fails.

Note: As a small language model (SLM), it has limitations, and the video shows both its strengths and its hallucinations.

🎬 Watch the full review on YouTube

Click the image above to watch on YouTube.

📜 License

This model is released under Apache-2.0 License — you can use, modify, and share this code freely, even in commercial projects. The only requirement is to keep the original attribution. Full text in the file: LICENSE.