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AI Models 34

  • SmolLM-1.7B↗

    HuggingFaceTB

    1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.

  • MOSS-Audio-Tokenizer-v2↗

    OpenMOSS-Team

    This is the code for the 48khz stereo version of MOSS-Audio-Tokenizer presented in MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. **MOSS-Audio-Tokenizer-v2** is a unified discrete audio tokenizer based on the **Cat** (**C**ausal **A**udio **T**okenizer with **T**ransformer) architecture. Scaling to 2 billion parameters, it functions as a unified discrete interface, delivering both lossless-quality reconstruction and high-level semantic alignment. * **Extreme Compression & Variable Bitrate**: It compresses 48kHz stereo audio into a remarkably low frame rate of 12.5Hz. Utilizing a 32-layer Residual Vector Quantization stack, it supports high-fidelity reconstruction across a wide range of bitrates. * **Pure Transformer Architecture**: The model features a "CNN-free" homogeneous architecture built entirely from Causal Transformer blocks. With 2B combined parameters (Encoder + Decoder), it ensures exceptional scalability and supports low-latency streaming inference. * **Large-Scale General Audio Training**: Trained on 3 million hours of diverse audio data, the model excels at encoding and reconstructing all audio domains, including speech, sound effects, and music. * **Unified Semantic-Acoustic Representation**: While achieving state-of-the-art reconstruction quality, Cat produces discrete tokens that are "semantic-rich," making them ideal for downstream tasks like speech understanding (ASR) and generation (TTS). * **Fully Trained From Scratch**: Cat does not rely on any pretrained encoders (such as HuBERT or Whisper) or distillation from teacher models. All representations are learned autonomously from raw data. * **End-to-End Joint Optimization**: All components—including the encoder, quantizer, decoder, discriminator, and a decoder-only LLM for semantic alignment—are optimized jointly in a single unified training pipeline.

  • nomic-embed-code↗

    nomic-ai

    `nomic-embed-code` is a state-of-the-art code embedding model that excels at code retrieval tasks: - **High Performance**: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet - **Multilingual Code Support**: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go) - **Advanced Architecture**: 7B parameter code embedding model - **Fully Open-Source**: Model weights, training data, and evaluation code released - **Total Parameters**: 7B - **Training Approach**: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining - **Supported Languages**: Python, Java, Ruby, PHP, JavaScript, and Go

  • GLM-5.3-Flash-Uncensored-AWQ↗

    hell0ks

    The abliterated (refusal-removed) build of Z.ai's GLM-5.3-Flash — baked directly into the official block-FP8 checkpoint, byte-for-byte drop-in for the original > The **abliterated** (refusal-removed) build of `zai-org/GLM-5.3-Flash` > — a **320B / 18B-active** Mixture-of-Experts model with hybrid linear + sparse attention, > 4-wide **Manifold-Constrained Hyper-Connections**, a native **vision + video** tower, an **MTP** > speculative head, and a **1M-token** context. > > The refusal direction is baked **directly into the official block-FP8 shards** — same format, same > shard layout, same `model.safetensors.index.json`. Every tensor matches the base checkpoint in > name, dtype and shape, so this is a **drop-in replacement** for `zai-org/GLM-5.3-Flash` in any > stack that already serves it. > > **On the `-FP8` name:** block-FP8 *is* the format Z.ai ships GLM-5.3-Flash in — the suffix names the > checkpoint's own precision, not a quantization step we applied. There is no upstream BF16 release to > derive from, so this is the full-precision source as published. This model has had its **safety alignment substantially removed** via *abliteration* (orthogonalizing the refusal direction out of the residual stream). As a direct consequence:

  • Kimi-K2.7-Code-GGUF↗

    unsloth

    To run Kimi K2.7 Code in full precision lossless, run Q8 (UD-Q8_K_XL), which is 595GB and only 10GB bigger than Q4 (UD-Q4_K_XL). See our Kimi K2.7 Code guide for quantization analysis and instructions. Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6. 1. **General Testing Details** - Unless stated otherwise, Kimi K2.7 Code and K2.6 were tested with thinking mode enabled via Kimi Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length; GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode. Aside from these differences, all benchmarks were evaluated under the same conditions. 2. **Coding Benchmarks** - Kimi Code Bench V2 is our in-house benchmark designed to evaluate coding agents on realistic tasks. It has diversed software engineering tasks across 10+ mainstream programming languages and a full production tech stack covering tasks from internal engineering use cases, production incidents, and real-world open-source projects, with emphasis on backend services, infrastructure, performance engineering, systems programming, security, frontend development, and ML/data engineering. - Program Bench evaluates code-generation agents by asking them to recreate a program’s behavior from only a compiled binary and its documentation. It spans 200 tasks, from small CLI tools to large systems like FFmpeg and SQLite. Submissions are judged against over 248,000 fuzz-generated behavioral tests. In each task, the agent is given an executable and its documentation, but no source code, decompilation, or internet access. It must choose its own implementation language, build the full program from scratch, and pass a behavioral test suite comparing its output against the original binary. - MLS-Bench evaluates whether AI systems can invent generalizable and scalable ML methods. MLS-Bench-Lite is the official 30-task subset of MLS-Bench, covering LLM pretraining and post-training, robotics, world models, computer vision, reinforcement learning, optimization, ML systems, AI for Science, and more. Agents are given 5 hours to explore before submitting

  • Qwopus3.6-27B-v1-preview-GGUF↗

    Jackrong

    As part of the Qwopus series, this release continues the same core direction established in earlier versions: stronger reasoning quality, a more stable answer structure, and less stylistic drift across long-form responses. Instead of introducing a complicated multi-stage design, this preview emphasizes a cleaner and more controlled supervised fine-tuning recipe, with the goal of producing outputs that feel more coherent, deliberate, and aligned across different tasks. > [!TIP] > This model was trained using Unsloth, and the full training workflow runs end-to-end successfully in practice. Many thanks to the Unsloth team for building and maintaining such a practical training stack for open-model fine-tuning. The current version is trained on a mixed reasoning dataset built primarily from `Kassadin88/Claude-Distillation-Dataset`, with additional samples drawn from:

  • Qwen3.6-35B-A3B-MTP-GGUF↗

    havenoammo

    > [!IMPORTANT] > ### MTP Layers Grafted on Unsloth UD XL Quantizations > > The GGUF files in this repo are **Unsloth Dynamic 2.0 XL (UD XL) quantizations** of Qwen3.6-35B-A3B > with **Multi-Token Prediction (MTP) layers grafted on top**. > > - **Base quantization:** Unsloth UD XL — see > Unsloth Dynamic 2.0 GGUFs for benchmarks. > - **MTP layers:** Taken from > am17an/Qwen3.6-35BA3B-MTP-GGUF > and stored in **Q8_0** precision, then merged into the UD XL GGUFs. > - **Why Q8 for MTP?** The draft heads are small relative to the base model, so Q8_0 keeps them > near-lossless while avoiding the overhead of full-requantizing the entire stack. > - **`convert.py`:** The script used to graft MTP layers into the UD XL GGUFs. Adapted from > this gist. > - **`35BA3B-MTP.gguf`:** The raw Q8_0 MTP layer source file (extracted only) used as input to `convert.py`. > > To run these files with MTP you need a custom build of **llama.cpp** that includes the MTP/speculative > decoding support from PR #22673. Follow the steps below. > > --- > > ### 🐳 Or Use Pre-built Docker Images (Recommended) > > Don't want to build from source? Ready-to-use Docker images with MTP support are available for multiple backends: > > > | Image | Backend | > |---|---| > | `havenoammo/llama:cuda13-server` | CUDA 13 | > | `havenoammo/llama:cuda12-server` | CUDA 12 | > | `havenoammo/llama:vulkan-server` | Vulkan | > | `havenoammo/llama:intel-server` | Intel (CPU/Xe) | > | `havenoammo/llama:rocm-server` | ROCm (AMD) | > > Quick start — running this model via Docker: > > The two flags that matter most for MTP are `--spec-type mtp` and `--spec-draft-n-max 3`. > See the Reddit post for more details and discussion. > > --- > > ### Building llama.cpp with MTP Support — Step by Step > > #### 1. Clone and enter the repo > > > #### 2. Fetch the latest remote changes > > This pulls all new refs from the upstream repository so you're working off the current tip of master. > > #### 3. Fetch PR #22673 as a local branch > > PR #22673 ("llama + spec: MTP Support") adds the speculative-decoding / MTP infrastructure that > lets `llama-server` consume multi-token prediction heads. We pull it directly without waiting for > i

  • Ternary-Bonsai-2-27B-262k-GGUF↗

    CaryPalmer

    **A 27B model with its whole trained context window, at q8_0 KV precision, on one RTX 4070 12 GB. 78 tok/s of new text at 32k tokens of context (175 on code), 69 at 64k, 61 at 112k, and the window keeps going to 262k. Lossless speculative decoding at every depth. A server that answers the requests agents and apps actually send. One command on Windows.** This is PrismML's Ternary Bonsai 2 27B (1.58-bit ternary `PTQ1_0`, 5.9 GB, Apache 2.0), the same weights, nothing re-quantized, with its MTP draft head grafted back on (byte-identical trunk, proof below) and the serving stack from github.com/professorpalmer/bonsai-ada-surgery: a tiered KV cache that puts the first ~119k positions in VRAM and the rest in pinned system RAM with bit-identical output, MTP drafting that pays at every depth, harness-proofing for the apps that send `effort: "high"` or tiny output caps, and an optional server-side layer (exact API cards, an API check, a sandboxed Python tool) that lifts the model's coding and agentic scores without touching the weights. Measured 2026-10-08 (`bench/depth_table.sh`), a fresh prompt at each depth: 400-token greedy answers with the default drafting (lookup + MTP head); new text = an essay, code = a Python module; prefill = cumulative for the whole prompt; display on the CPU's iGPU. Every number has a receipt in the repository (`docs/Q8_FULL_CONTEXT.md`, `docs/RECEIPTS.md`, `docs/QUALITY.md`).

  • Ornith-1.5-122B-UNCENSORED-GGUF↗

    dealignai

    > **Important:** UNCENSORED = refusal-removed **GGUF** build of **Ornith-1.5-122B** (Qwen3.5-family hybrid GatedDeltaNet + full-attention MoE, 122B total / 10B active). This repo stacks **Q4_K_M (~79 GB)** and **IQ2_XXS (~44 GB)** in one place. Both carry the native **MTP (nextn) head** for self-speculative decoding and ship with the matching **mmproj** vision tower. Follow **@dealignai** for new releases. **Qwen3.5 hybrid GatedDeltaNet + full-attention MoE** · **122B total / 10B active** · **VL (image + video)** · **MTP (nextn) speculative decoding** A refusal-removed GGUF build of **Ornith-1.5-122B** (Qwen3.5-based 122B-A10B hybrid MoE with vision). Two sizes in one repo. The vision tower (`mmproj-F16.gguf`) is **byte-identical to the base BF16 source**, so VL capability is preserved exactly.

  • SmolLM-135M↗

    HuggingFaceTB

    1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post..