Results for “8-bit”
32 skills
orchestra-research Quantizing Models Bitsandbytes
Quantize LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss using bitsandbytes. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers.
10.4k · bundle
tianhao909 Quantizing Models Bitsandbytes
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
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qcmuu Quantizing Models Bitsandbytes
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
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qhjqhj00 Latency
Measures inference latency of binarized, 8-bit, and 32-bit convolutional layers on edge devices to evaluate the efficiency and speedup of the Larq Compute Engine framework compared to standard implementations.
3
qhjqhj00 Hqq Quantization
Quantize LLMs to 8/4/3/2/1-bit precision without calibration data, using multiple backends and HuggingFace/vLLM integration.
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q2805187159 Gguf Quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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More results
tianhao909 Gguf Quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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qcmuu Gguf Quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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bog5d Gguf Quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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ichichuang Gguf Quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
0 · bundle
orchestra-research Hqq Quantization
Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.
10.4k · bundle
dvcrn Odu
Classifies situations into 256 binary states and maps each to a prescribed action, reporting the pattern, decimal, name, range, and action to execute.
32
orchestra-research Llama Factory
Provides expert guidance for fine-tuning LLMs with LLaMA-Factory, covering WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, and multimodal support.
10.4k · bundle
github Nano Banana Pro Openrouter
Generate or edit images via OpenRouter using the Gemini 3 Pro Image model, with support for prompt-only generation, single-image edits, and multi-image compositing at 1K/2K/4K resolutions.
36.2k · bundle
heygen Media Use
Resolves, generates, and operates on media assets (audio, images, icons, logos, voice, color grades, LUTs) for HyperFrames projects, using a local cache and the HeyGen CLI for free-usage catalog search and TTS.
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orchestra-research Gptq
Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.
10.4k · bundle
aniruddhaadak80 Ascii Video
ASCII video: convert video/audio to colored ASCII MP4/GIF.
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jiachen-t-wang Idefics2 An 8b Parameters Multimodal Model Arxiv 2405 02246v
Idefics2: An 8B Parameters Multimodal Model
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baofeng-tech Openclaw Media Gen
Generate images and videos with AIsa. Four image models (Google Gemini 3 Pro Image, Alibaba Wan 2.7 image + image-pro, ByteDance Seedream) and four Wan video variants (wan2.6/2.7 × t2v/i2v). One API key; the client routes each model to the correct endpoint automatically. Use when: the user needs AI image or video generation workflows.
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jiachen-t-wang Pixtral 12b A Frontier Multimodal Model Arxiv Pixtral 2024
Pixtral 12B: A Frontier Multimodal Model
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majiayu000 Hqq Quantization
Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.
567 · bundle
czlonkowski N8n Binary And Data
Handle files and binary data in n8n workflows correctly, covering the $binary vs $json split, reading/writing binary, preserving binary across transforms, and the agent-tool binary boundary.
5.7k · bundle
orchestra-research Awq Quantization
Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.
10.4k · bundle
lord1egypt Ascii Video
Converts video, audio, or images into colored ASCII art videos (MP4/GIF) with generative effects, audio-reactive visuals, and text overlays.
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akillness Perfectpixel
Generates game-ready sprite bundles from a text description, including character animations, 8-direction sprite sets, and exports to sprite sheets, manifest.json, Aseprite JSON, GIF/APNG, and individual PNG frames.
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nvidia Tao Mine Aoi Images
Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.
2.2k · bundle
24601 Surrealdb
Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.
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orchestra-research Gguf Quantization
Convert and quantize models to GGUF format for efficient CPU/GPU inference with llama.cpp, supporting 2-8 bit quantization and Apple Silicon acceleration.
10.4k · bundle
tianhao909 Llama Cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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qcmuu Llama Cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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ichichuang Llama Cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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orchestra-research Llama Cpp
Run LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
10.4k · bundle