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Model: algoscienceacademy/Harness Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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tags:
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- llm
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- chatbot
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- text-generation
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- causal-lm
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- harness
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- algosciencelab
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- vlsi
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- coding
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- reasoning
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- pytorch
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widget:
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- example_title: Python Example
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messages:
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- role: system
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content: You are Harness, an AI assistant created by Algo Science Lab.
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- role: user
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content: Write a Python function that calculates the factorial of a number.
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---
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<div align="center">
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# Harness-1B
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### Developed by Algo Science Lab
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**Author:** Shahrear Hossain
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**Hugging Face:** https://huggingface.co/algoscienceacademy
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</div>
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---
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# Harness-1B
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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.
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The model contains approximately **1 billion parameters**, making it suitable for local deployment while providing strong performance for everyday AI tasks.
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Harness-1B has been fine-tuned to provide accurate, helpful, and concise responses across a wide variety of technical and general domains.
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---
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# Features
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- General conversation
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- Code generation
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- Python programming
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- C programming
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- C++ programming
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- Rust programming
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- Java programming
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- JavaScript
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- TypeScript
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- Verilog HDL
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- SystemVerilog
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- VHDL
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- RTL Design
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- FPGA Development
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- ASIC Design
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- CMOS Digital Design
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- Semiconductor Engineering
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- VLSI Design
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- Mathematics
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- Electronics
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- Physics
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- Problem Solving
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- Technical Documentation
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- AI Research Assistance
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---
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# Model Details
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| Property | Value |
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|----------|-------|
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| Model Name | Harness-1B |
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| Organization | Algo Science Lab |
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| Hugging Face Username | algoscienceacademy |
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| Parameters | ~1 Billion |
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| Architecture | Llama-based |
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| Model Type | Causal Language Model |
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| Context Length | 2048 Tokens *(or your trained context size)* |
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| Precision | FP16 / BF16 / GGUF |
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| Framework | PyTorch |
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| License | Apache-2.0 |
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---
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# Intended Uses
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Harness-1B is designed for:
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- AI Chatbots
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- Programming Assistant
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- Educational Applications
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- Research
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- Embedded AI
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- Local AI Deployment
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- Engineering Assistance
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- Electronics Design
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- FPGA Development
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- ASIC/VLSI Workflow
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- RTL Development
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- Automation
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- Documentation
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---
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# Example
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```python
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="algoscienceacademy/Harness-1B",
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device_map="auto"
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)
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messages = [
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{
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"role": "system",
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"content": "You are Harness, an AI assistant created by Algo Science Lab."
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},
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{
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"role": "user",
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"content": "Write a Python program to print Fibonacci numbers."
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}
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]
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prompt = pipe.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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output = pipe(
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prompt,
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max_new_tokens=256,
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temperature=0.7,
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)
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print(output[0]["generated_text"])
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```
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---
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# GGUF Usage
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Harness-1B is also available in GGUF format for:
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- llama.cpp
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- LM Studio
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- Jan
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- Open WebUI
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- KoboldCpp
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- Ollama (after conversion)
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Example:
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```bash
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./main \
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-m Harness-1B-Q4_K_M.gguf \
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-p "Explain CMOS Inverter."
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```
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---
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# Training
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Harness-1B is trained and fine-tuned using open-source datasets and instruction-following techniques.
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Possible data sources include:
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- SlimPajama
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- StarCoderData
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- UltraChat
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- UltraFeedback
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Additional custom datasets may have been used during supervised fine-tuning.
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---
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# Capabilities
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Harness-1B can:
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- Answer questions
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- Explain concepts
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- Generate code
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- Debug code
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- Write documentation
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- Solve mathematics
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- Explain algorithms
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- Assist with VLSI
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- Help with FPGA design
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- Generate Verilog
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- Generate SystemVerilog
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- Produce technical reports
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---
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# Limitations
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Harness-1B may:
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- Produce incorrect information.
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- Generate outdated knowledge.
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- Make reasoning mistakes.
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- Require verification for safety-critical applications.
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- Require human review for production environments.
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---
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# Hardware Requirements
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Recommended:
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- 8 GB RAM (Q4 GGUF)
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- 12 GB RAM (Q6 GGUF)
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- 16 GB RAM (FP16)
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- CUDA GPU recommended but optional
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---
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# Citation
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```bibtex
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@misc{Harness1B,
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title={Harness-1B},
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author={Algo Science Lab},
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year={2026},
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publisher={Hugging Face},
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howpublished={https://huggingface.co/algoscienceacademy/Harness-1B}
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}
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```
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---
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# License
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Apache License 2.0
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---
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# Acknowledgements
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Harness-1B builds upon open-source language model research and would not be possible without the work of the open-source AI community, including:
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- Meta AI (Llama Architecture)
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- TinyLlama Project
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- Hugging Face
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- PyTorch
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- Transformers
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- llama.cpp
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---
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# Contact
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**Organization:** Algo Science Lab
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**Hugging Face:** https://huggingface.co/algoscienceacademy
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---
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Made with ❤️ by Algo Science Lab.
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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{
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"epoch": 3.0,
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"eval_logits/chosen": -2.707406759262085,
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"eval_logits/rejected": -2.656524419784546,
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"eval_logps/chosen": -370.1297607421875,
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"eval_logps/rejected": -296.0738525390625,
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"eval_loss": 0.513750433921814,
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"eval_rewards/accuracies": 0.738095223903656,
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"eval_rewards/chosen": -0.02744222804903984,
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"eval_rewards/margins": 1.0087225437164307,
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"eval_rewards/rejected": -1.03616464138031,
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"eval_runtime": 93.5908,
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"eval_samples": 2000,
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"eval_samples_per_second": 21.37,
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"eval_steps_per_second": 0.673
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}
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{
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 2048,
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"pad_token_id": 0,
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"transformers_version": "4.35.0"
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}
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model.safetensors
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"rstrip": false,
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"single_word": false
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"1": {
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"special": true
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}
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},
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"bos_token": "<s>",
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"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": false,
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"model_max_length": 2048,
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"pad_token": "</s>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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