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AI 模型 84759

• kana-whisper↗

  0x3

• WizardLM-13B-Uncensored-4bit-128g↗

  4bit

• GPT-NeoX-1.3B-viet-final-GGUF↗

  afrideva

• qwen2-57b-a14b-instruct↗

  阿里巴巴 · Qwen

  Qwen2 是 Qwen 大型语言模型的新系列。针对 Qwen2，我们发布了参数量从 0.5 亿到 720 亿不等的多个基础语言模型和指令微调语言模型，其中包括混合专家模型。本仓库包含经过指令微调的 57B-A14B 混合专家 Qwen2 模型。与最先进的开源语言模型（包括此前发布的 Qwen1.5）相比，Qwen2 在面向语言理解、语言生成、多语言能力、编码、数学、推理等一系列基准测试中，整体上已超越大多数开源模型，并展现出媲美专有模型的竞争力。Qwen2-57B-A14B-Instruct 支持长达 65,536 个 token 的上下文长度，能够处理超长输入。有关如何部署 Qwen2 处理长文本的详细说明，请参阅本节。

• Hemmingway-1-Unlocked-GGUF↗

  amylynn

• LFM2.5-2.6B-GGUF-shards↗

  Arki05

  - `source/`: LiquidAI's BF16 GGUF (LiquidAI/LFM2.5-2.6B-GGUF), unchanged. - `types/ -hq.gguf`, `types/ -hq1.gguf`: the tensors at llama.cpp type `T`, rebuilt from the BF16 weights in two Hessian-quantized (GPTQ-family) builds: `-hq`, the higher-quality one, the block tensors; `-hq1`, the first build, every quantized tensor. Same type, format and size as llama.cpp's: plain GGUF types, for stock llama.cpp (a GGUF assembled from them holds the plain type `T`); modified versions of the original weights. - `data/`: the calibration data the types were made from (token ids and their manifest). - `manifest.json` (`qcore-store/1`), `skeleton.gguf`, `store.json`, `records/`: what qalloc reads, with every tensor's offset and sha256. The first version of this store (bartowski's BF16 GGUF and imatrix, types by llama.cpp's `llama-quantize`) is at revision `fe50613`.

• Art-v0-3B-GGUF↗

  bartowski

• Qwen2.5-Coder-1.5B-Instruct-abliterated-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

• starcoder2-7b↗

  bigcode

  1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation StarCoder2-7B model is a 7B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3.5+ trillion tokens. - **Project Website:** bigcode-project.org - **Paper:** Link - **Point of Contact:** contact@bigcode-project.org - **Languages:** 17 Programming languages

• Drjajsufhfh↗

  Bill435