Meta-Llama-3.1-8B-Instruct-AWQ-INT4
hugging-quants · Kit
Runs Meta's Llama 3.1 8B Instruct chat model in 4-bit AWQ form, so it fits on a GPU with about 4 GiB of free memory for the weights. For developers who want a…
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AI skills and agents for developers
Prompts, coding agent briefs and kits for writing and reviewing code with fewer tokens.
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Phi-4-mini-instruct
microsoft · Kit
A 3.8B open instruction model from Microsoft with a 128K token context, built for reasoning, math and logic in tight memory or latency budgets. It suits develo…
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Ornith-1.5-9B-GGUF by AtomicChat
AtomicChat · Kit
GGUF builds of Ornith 1.5 9B, a model that reads images and text, in sizes from 2.81 GB to 17.9 GB plus a vision projector. Pick the file that fits your graphi…
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Hy-Memory-30B-A3B
tencent · Kit
Writes an assistant's long-term memory, choosing what to keep and how to update it inside Mem0, MemOS or Hy-Memory, with about 3B active parameters. For develo…
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Ornith-1.5-122B-NVFP4-DFlash2-RTX-Spark
Sherlock-Comms · Kit
Serves the 122B Ornith 1.5 model through vLLM with DFlash2 speculative decoding, using an unquantized output head. Made for RTX Spark laptops with 128 GB of me…
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Sarvam-105B GGUF
sarvamai · Kit
The GGUF version of Sarvam-105B, a large mixture-of-experts model with a focus on Indian languages, split into 9 shards at 4-bit for llama.cpp. For people with…
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Chatterbox-Nano
ResembleAI · Kit
Speaks English text aloud on a plain CPU with a 110M-parameter model, and adds laughs and coughs through tags in the text. Suits developers who need fast on-de…
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Qwen3.8-27B-mixed-3.5bpw-MLX
rapid-mlx · Kit
A 27B vision-language model compressed to 3.5 bits per weight for Apple Silicon Macs with 24 GB of memory, served locally through Rapid-MLX. Suits Mac users an…
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Qwen2.5-7B-Instruct-AWQ
Qwen · Kit
A 4-bit AWQ build of Qwen2.5-7B-Instruct, a 7.6B chat model that follows instructions, handles tables and returns JSON. For developers who want a capable chat…
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Nanbeige4.3-33B-INT4
Nanbeige · Kit
INT4 quantized version of Nanbeige4.3-33B, a 33B model for coding and professional agent tasks, made with quantization-aware training. For developers who want…
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Qwen3.8-27B Fable-5 Coding Distilled GGUF
mradermacher · Kit
GGUF quantized files of a 27.3B coding-distilled Qwen3.8 model, from 2-bit to 8-bit with an optional multi-modal supplement. They suit developers who run a cod…
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JevK5-chat-mlx-4bit
mlx-community · Kit
A 4-bit MLX copy of the 4.2B JevK5 chat model for Apple Silicon, with a 262,144 token context and a small memory footprint. Made for Mac users who want a quick…
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DeepSeek-V4.1-Flash-1017-oQ4e-ngram-mtp
mlx-community · Kit
A mixed-precision MLX copy of DeepSeek-V4.1-Flash-1017 that keeps the Engram tables at FP8. Made for people with hundreds of GB of memory who want to run the m…
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Qwen3.8-27B-oQ4e-mtp
Jundot · Kit
Runs the 27B Qwen3.8 vision-language model on MLX in about 17.5 GB, reading text, images and video with a 262,144-token context. A mixed-precision quantization…
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Qwen3.6-35B-A3B-2Bit-GSQ
ISTA-DASLab · Kit
A 2-bit quantized copy of Qwen3.6-35B-A3B made with GSQ, a Gumbel-Softmax method, that keeps most reasoning and coding ability at a much smaller memory footpri…
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SmolVLM2-2.2B-Instruct
HuggingFaceTB · Kit
Answers questions about videos, images and text together, with a 2.2B model that needs about 5.2 GB of GPU memory for video. For developers building visual ass…
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svd-linear-attention-roberta-base-adasplash
Hooshaai · Kit
Classifies English text, such as SST-2 sentiment, with a RoBERTa Base model whose attention is made sparse by AdaSplash, trading some accuracy for lower latenc…
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roberta-base-fwsvd-rank32
Hooshaai · Kit
Classifies English text with a RoBERTa Base model shrunk to about 82M parameters by Fisher-weighted SVD at rank 32, trading some accuracy for lower latency and…
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roberta-base-adasplash
Hooshaai · Kit
A RoBERTa Base sentiment classifier whose attention is swapped for a sparse AdaSplash version, which zeroes out 40% of attention weights to cut time and memory…
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hs-rerank-1
hindsight-ai · Kit
Scores how well each document answers a query, in eight languages and for inputs up to 8,192 tokens, so a search or memory system can put the best matches firs…
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