Showing 4 open source projects for "fit"

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  • 1
    Z80-μLM

    Z80-μLM

    Z80-μLM is a 2-bit quantized language model

    ...The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment.
    Downloads: 1 This Week
    Last Update:
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  • 2
    PokeeResearch-7B

    PokeeResearch-7B

    Pokee Deep Research Model Open Source Repo

    ...The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows.
    Downloads: 2 This Week
    Last Update:
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  • 3
    ChatGLM Efficient Tuning

    ChatGLM Efficient Tuning

    Fine-tuning ChatGLM-6B with PEFT

    ...It wraps techniques like LoRA and prompt-tuning into simple training scripts so you can adapt a large model to your domain without full retraining. The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it straightforward to stand up a task-specific assistant. Because the code leans on widely used libraries, you can bring your own datasets and monitoring tools with minimal glue. For builders who want results fast, it’s a pragmatic way to specialize ChatGLM while controlling costs and turnaround time.
    Downloads: 0 This Week
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  • 4
    gpt-oss-120b

    gpt-oss-120b

    OpenAI’s open-weight 120B model optimized for reasoning and tooling

    GPT-OSS-120B is a powerful open-weight language model by OpenAI, optimized for high-level reasoning, tool use, and agentic tasks. With 117B total parameters and 5.1B active parameters, it’s designed to fit on a single H100 GPU using native MXFP4 quantization. The model supports fine-tuning, chain-of-thought reasoning, and structured outputs, making it ideal for complex workflows. It operates in OpenAI’s Harmony response format and can be deployed via Transformers, vLLM, Ollama, LM Studio, and PyTorch. Developers can control the reasoning level (low, medium, high) to balance speed and depth depending on the task. ...
    Downloads: 0 This Week
    Last Update:
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