Text Generation
Transformers
Safetensors
GGUF
PyTorch
English
llama
llm
chatbot
causal-lm
harness
algosciencelab
vlsi
coding
reasoning
conversational
text-generation-inference
Instructions to use algoscienceacademy/Harness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use algoscienceacademy/Harness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="algoscienceacademy/Harness") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("algoscienceacademy/Harness") model = AutoModelForCausalLM.from_pretrained("algoscienceacademy/Harness", device_map="auto") 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 algoscienceacademy/Harness with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="algoscienceacademy/Harness", filename="harness_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 algoscienceacademy/Harness 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 algoscienceacademy/Harness:F16 # Run inference directly in the terminal: llama cli -hf algoscienceacademy/Harness:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf algoscienceacademy/Harness:F16 # Run inference directly in the terminal: llama cli -hf algoscienceacademy/Harness:F16
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 algoscienceacademy/Harness:F16 # Run inference directly in the terminal: ./llama-cli -hf algoscienceacademy/Harness:F16
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 algoscienceacademy/Harness:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf algoscienceacademy/Harness:F16
Use Docker
docker model run hf.co/algoscienceacademy/Harness:F16
- LM Studio
- Jan
- vLLM
How to use algoscienceacademy/Harness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "algoscienceacademy/Harness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "algoscienceacademy/Harness", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/algoscienceacademy/Harness:F16
- SGLang
How to use algoscienceacademy/Harness 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 "algoscienceacademy/Harness" \ --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": "algoscienceacademy/Harness", "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 "algoscienceacademy/Harness" \ --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": "algoscienceacademy/Harness", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use algoscienceacademy/Harness with Ollama:
ollama run hf.co/algoscienceacademy/Harness:F16
- Unsloth Studio
How to use algoscienceacademy/Harness 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 algoscienceacademy/Harness 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 algoscienceacademy/Harness to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for algoscienceacademy/Harness to start chatting
- Atomic Chat new
- Docker Model Runner
How to use algoscienceacademy/Harness with Docker Model Runner:
docker model run hf.co/algoscienceacademy/Harness:F16
- Lemonade
How to use algoscienceacademy/Harness with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull algoscienceacademy/Harness:F16
Run and chat with the model
lemonade run user.Harness-F16
List all available models
lemonade list
| license: apache-2.0 | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T | |
| tags: | |
| - llm | |
| - chatbot | |
| - text-generation | |
| - causal-lm | |
| - harness | |
| - algosciencelab | |
| - vlsi | |
| - coding | |
| - reasoning | |
| - pytorch | |
| widget: | |
| - example_title: Python Example | |
| messages: | |
| - role: system | |
| content: You are Harness, an AI assistant created by Algo Science Lab. | |
| - role: user | |
| content: Write a Python function that calculates the factorial of a number. | |
| <div align="center"> | |
| # Harness-1B | |
| ### Developed by Algo Science Lab | |
| **Author:** Shahrear Hossain | |
| **Hugging Face:** https://huggingface.co/algoscienceacademy | |
| </div> | |
| --- | |
| # 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 | |
| ```python | |
| 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: | |
| ```bash | |
| ./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 | |
| ```bibtex | |
| @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. |