Harness-1B
Developed by Algo Science Lab
Author: Shahrear Hossain
Hugging Face: https://huggingface.co/algoscienceacademy
Harness-1B
Harness-1B is a lightweight large language model developed by Algo Science Lab for instruction following, coding assistance, reasoning, mathematics, electronics, semiconductor engineering, VLSI design, and general conversational AI.
The model contains approximately 1 billion parameters, making it suitable for local deployment while providing strong performance for everyday AI tasks.
Harness-1B has been fine-tuned to provide accurate, helpful, and concise responses across a wide variety of technical and general domains.
Features
- General conversation
- Code generation
- Python programming
- C programming
- C++ programming
- Rust programming
- Java programming
- JavaScript
- TypeScript
- Verilog HDL
- SystemVerilog
- VHDL
- RTL Design
- FPGA Development
- ASIC Design
- CMOS Digital Design
- Semiconductor Engineering
- VLSI Design
- Mathematics
- Electronics
- Physics
- Problem Solving
- Technical Documentation
- AI Research Assistance
Model Details
| Property | Value |
|---|---|
| Model Name | Harness-1B |
| Organization | Algo Science Lab |
| Hugging Face Username | algoscienceacademy |
| Parameters | ~1 Billion |
| Architecture | Llama-based |
| Model Type | Causal Language Model |
| Context Length | 2048 Tokens (or your trained context size) |
| Precision | FP16 / BF16 / GGUF |
| Framework | PyTorch |
| License | Apache-2.0 |
Intended Uses
Harness-1B is designed for:
- AI Chatbots
- Programming Assistant
- Educational Applications
- Research
- Embedded AI
- Local AI Deployment
- Engineering Assistance
- Electronics Design
- FPGA Development
- ASIC/VLSI Workflow
- RTL Development
- Automation
- Documentation
Example
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="algoscienceacademy/Harness-1B",
device_map="auto"
)
messages = [
{
"role": "system",
"content": "You are Harness, an AI assistant created by Algo Science Lab."
},
{
"role": "user",
"content": "Write a Python program to print Fibonacci numbers."
}
]
prompt = pipe.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
output = pipe(
prompt,
max_new_tokens=256,
temperature=0.7,
)
print(output[0]["generated_text"])
GGUF Usage
Harness-1B is also available in GGUF format for:
- llama.cpp
- LM Studio
- Jan
- Open WebUI
- KoboldCpp
- Ollama (after conversion)
Example:
./main \
-m Harness-1B-Q4_K_M.gguf \
-p "Explain CMOS Inverter."
Training
Harness-1B is trained and fine-tuned using open-source datasets and instruction-following techniques.
Possible data sources include:
- SlimPajama
- StarCoderData
- UltraChat
- UltraFeedback
Additional custom datasets may have been used during supervised fine-tuning.
Capabilities
Harness-1B can:
- Answer questions
- Explain concepts
- Generate code
- Debug code
- Write documentation
- Solve mathematics
- Explain algorithms
- Assist with VLSI
- Help with FPGA design
- Generate Verilog
- Generate SystemVerilog
- Produce technical reports
Limitations
Harness-1B may:
- Produce incorrect information.
- Generate outdated knowledge.
- Make reasoning mistakes.
- Require verification for safety-critical applications.
- Require human review for production environments.
Hardware Requirements
Recommended:
- 8 GB RAM (Q4 GGUF)
- 12 GB RAM (Q6 GGUF)
- 16 GB RAM (FP16)
- CUDA GPU recommended but optional
Citation
@misc{Harness1B,
title={Harness-1B},
author={Algo Science Lab},
year={2026},
publisher={Hugging Face},
howpublished={https://huggingface.co/algoscienceacademy/Harness-1B}
}
License
Apache License 2.0
Acknowledgements
Harness-1B builds upon open-source language model research and would not be possible without the work of the open-source AI community, including:
- Meta AI (Llama Architecture)
- TinyLlama Project
- Hugging Face
- PyTorch
- Transformers
- llama.cpp
Contact
Organization: Algo Science Lab
Hugging Face: https://huggingface.co/algoscienceacademy
Made with ❤️ by Algo Science Lab.