Instructions to use prithivMLmods/Kapteyn-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Kapteyn-500M with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Kapteyn-500M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Kapteyn-500M")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Kapteyn-500M")
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 prithivMLmods/Kapteyn-500M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Kapteyn-500M",
	filename="Kapteyn-500M.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 prithivMLmods/Kapteyn-500M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Kapteyn-500M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Kapteyn-500M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Kapteyn-500M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Kapteyn-500M: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 prithivMLmods/Kapteyn-500M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Kapteyn-500M: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 prithivMLmods/Kapteyn-500M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Kapteyn-500M:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Kapteyn-500M:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Kapteyn-500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Kapteyn-500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Kapteyn-500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Kapteyn-500M:Q4_K_M

SGLang

How to use prithivMLmods/Kapteyn-500M 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 "prithivMLmods/Kapteyn-500M" \
    --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": "prithivMLmods/Kapteyn-500M",
		"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 "prithivMLmods/Kapteyn-500M" \
        --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": "prithivMLmods/Kapteyn-500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Kapteyn-500M with Ollama:
```
ollama run hf.co/prithivMLmods/Kapteyn-500M:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Kapteyn-500M 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 prithivMLmods/Kapteyn-500M 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 prithivMLmods/Kapteyn-500M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Kapteyn-500M to start chatting

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/Kapteyn-500M with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Kapteyn-500M:Q4_K_M
```

Lemonade

How to use prithivMLmods/Kapteyn-500M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Kapteyn-500M:Q4_K_M

Run and chat with the model

lemonade run user.Kapteyn-500M-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kapteyn-500M

Kapteyn-500M is a lightweight, general-purpose micro language model based on the LlamaForCausalLM architecture and trained on the Llama2 Group of models. This compact 500M parameter model is designed for simple chats and responses, making it ideal for conversational AI applications where efficiency and quick response times are prioritized over complex reasoning tasks.

Key Features

Compact & Efficient Architecture Built on the proven LlamaForCausalLM architecture with only 500M parameters, ensuring fast inference and low memory footprint for resource-constrained environments.
General-Purpose Conversational AI Optimized for natural dialogue, casual conversations, and simple Q&A tasks—perfect for chatbots, virtual assistants, and interactive applications.
Llama2-Based Training Leverages the robust foundation of the Llama2 Group of models, inheriting their conversational capabilities while maintaining ultra-lightweight deployment requirements.
Fast Response Generation Designed for quick inference with minimal latency, making it suitable for real-time chat applications and interactive user experiences.
Versatile Deployment Options Runs efficiently on CPUs, entry-level GPUs, mobile devices, and edge computing platforms with minimal resource requirements.
Simple Integration Easy to integrate into existing applications with standard transformer interfaces and minimal setup requirements.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Kapteyn-500M"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Hello! How are you doing today?"

messages = [
    {"role": "system", "content": "You are a helpful and friendly assistant."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Intended Use

Casual conversation and general chat applications
Simple Q&A systems and customer service bots
Educational tools requiring basic conversational interaction
Mobile and edge AI applications with limited computational resources
Prototyping conversational AI features before scaling to larger models
Personal assistants for everyday tasks and simple information retrieval

Limitations

Limited complex reasoning and analytical capabilities compared to larger models
Not suitable for specialized technical, scientific, or mathematical tasks
Context window limitations may affect longer conversations
May struggle with nuanced or highly specialized domain knowledge
Optimized for simple responses rather than detailed explanations or complex problem-solving.

Downloads last month: 205

Safetensors

Model size

0.5B params

Tensor type

BF16

F16

Model tree for prithivMLmods/Kapteyn-500M

Quantizations

2 models