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AI 模型 85003
- QIE-2511-Outfit-Design-Layout↗
prithivMLmods
- internistai-base-7b-v0.2-GGUF↗
QuantFactory
- SmolLM-135M-Instruct-GGUF↗
QuantFactory
- Qwen3.5-122B-A10B↗
阿里巴巴 · Qwen
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...
- mlfoundations-dev_-_oh_v1.3_opengpt_x2-gguf↗
RichardErkhov
- simonveitner_-_MathHermes-2.5-Mistral-7B-gguf↗
RichardErkhov
- thkim0305_-_Mistral-7B_ours_algo7s_lyr20_n11_0.5_0.5_0.1_0.1_200steps_full-gguf↗
RichardErkhov
- Llama-xLAM-2-8b-fc-r-gguf↗
Salesforce
This repo provides the GGUF format for the Llama-xLAM-2-8b-fc-r model. Here's a link to original model Llama-xLAM-2-8b-fc-r. Large Action Models (LAMs) are advanced language models designed to enhance decision-making by translating user intentions into executable actions. As the **brains of AI agents**, LAMs autonomously plan and execute tasks to achieve specific goals, making them invaluable for automating workflows across diverse domains. **This model release is for research purposes only.** The new **xLAM-2** series, built on our most advanced data synthesis, processing, and training pipelines, marks a significant leap in **multi-turn conversation** and **tool usage**. Trained using our novel APIGen-MT framework, which generates high-quality training data through simulated agent-human interactions. Our models achieve state-of-the-art performance on **BFCL** and **τ-bench** benchmarks, outperforming frontier models like GPT-4o and Claude 3.5. Notably, even our smaller models demonstrate superior capabilities in multi-turn scenarios while maintaining exceptional consistency across trials.
- xLAM-1b-fc-r-gguf↗
Salesforce
- YuE2-GGUF↗
scragnog
GGUF conversions of m-a-p/YuE2-3B and m-a-p/YuE2-Vae for HOT-Step CPP, a local C++/GGML music generation engine. YuE2 is an autoregressive-planner + flow-matching music model from the M-A-P team; these files let it run through HOT-Step's GGML inference stack instead of the original PyTorch checkpoint. - Size: 3,960,938,800 bytes. - SHA256: `96fe199377309001ed8cd26a944baeee8cc31a20ba7c36d1d3c0a7e1f4149db6`. - No changes to the upstream checkpoint; the upstream CC BY-NC 4.0 licence applies. See LICENSE. All model weights here are derived from the original YuE2 release. Credit and thanks to the YuE2 authors (M-A-P) for the underlying research and checkpoints: