Nesso-4B is your small on-device everyday assistant: a highly versatile 4B parameter language model designed for efficient deployment on consumer hardware while maintaining strong performance across diverse tasks.

Key Features

On-Device Ready: Optimized for local deployment
Highly Versatile: Excels at RAG applications, agentic workflows, tool use, and general assistance
Multilingual: Supports multiple languages with strong cross-lingual capabilities

Model Specifications

Parameters: 4.0B
License: Mii Open License 1.0

Quickstart

Installation

Ensure you have the latest version of transformers:

pip install transformers>=4.51.0

Basic Usage (streaming)

python

from transformers import TextStreamer
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mii-llm/nesso-4B"

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

streamer = TextStreamer(tokenizer, skip_prompt=True)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Write a short story about AI."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

_ = model.generate(
    **inputs,
    streamer=streamer,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=50
)

Deployment

vLLM

bash

pip install "vllm>=0.8.5"
vllm serve mii-llm/nesso-4B --enable-auto-tool-choice --tool-call-parser hermes

Both create OpenAI-compatible API endpoints that you can use with standard clients.

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768 or 16,384.

Local Applications

Nesso is also supported by popular local inference applications:

Ollama: For easy command-line usage
LMStudio: For GUI-based interaction
llama.cpp: For C++ deployment
MLX-LM: For Apple Silicon optimization

Best Practices

Quantization

For reduced memory usage:

python

# INT8
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_8bit=True,
    device_map="auto"
)

# INT4
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,
    device_map="auto"
)

Tips for Best Results

Be Specific: Clear, detailed prompts yield better results
Use Examples: Provide few-shot examples for complex tasks
Iterate: Refine your prompts based on outputs
Set Expectations: Use system prompts to define the assistant's role
Manage Context: Keep context relevant and well-organized
Adjust Temperature: Lower for factual tasks, higher for creative ones
Use Tools: Leverage agentic capabilities for complex workflows

License

This model is released under the mii 1.0 License.

Citation

If you use Nesso in your work, please cite:

bibtex

@misc{nesso-4b,
  author = {mii-llm},
  title = {Nesso-4B: Your Small On-Device Everyday Assistant},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/mii-llm/nesso-4B}
}

Acknowledgments

Built with Axolotl

This model is licensed under the Mii Open License v1.0. Free for research and personal use. Production deployment requires prior written permission. Commercial use by entities requires a separate commercial license. Citation is required for all uses. Contact us for permissions.