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AI Models 2119

  • MiniCPM5-2B-Base↗

    openbmb

    We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA. 🏆 **2B-class open-source SOTA**: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks. #capability-comparison-radar { --foreground: #171717; display: block; width: 100%; max-width: 620px; margin: 0 auto; background: #fff; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", sans-serif; } #capability-comparison-radar svg { display: block; width: 100%; height: auto; background: #fff; } #capability-comparison-radar .title { fill: var(--foreground); font-size: 16px; font-weight: 600; letter-spacing: 0; } #capability-comparison-radar .axis-label { fill: #333; font-size: 12px; font-weight: 600; } #capability-comparison-radar .ring { fill: none; stroke: rgba(128,128,128,.18); stroke-width: .8; } #capability-comparison-radar .spoke { stroke: rgba(128,128,128,.25); stroke-width: .8; } #capability-comparison-radar .ring-label { fill: #8a8a8a; font-size: 9px; } #capability-comparison-radar .series { stroke-linejoin: round; } #capability-comparison-radar .series.primary { stroke-width: 2.4; } #capability-comparison-radar .legend-label { fill: #171717; font-size: 12px; font-weight: 600; } #capability-comparison-radar .legend-average { fill: #666; font-size: 11px; } #capability-comparison-radar .legend-swatch { rx: 3; } @media (max-width: 900px) { #capability-comparison-radar { overflow-x: auto; } #capability-comparison-radar svg { min-width: 620px; } }

  • Tiel-Coder-35B-A3B-GGUF↗

    peculiar-ragdoll

    *Tiel* is the fast coder of the arsenal. At 4-bit quantization and 22 GB it fixes real codebase issues at the rate (and speed, with the right GPU) of Opus 4.6 medium, while holding the best multi-turn conversation of any local model we have measured. It is also cheerfully bad at trivia. > **This is Ornith-1.5-35B-A3B re-quantized > dynamically with our own imatrix and carrying the Sharp chat template** > inside the GGUF. Find TielCoder MTP GGUFs here. > **The uncensored and improved CyberTiel is out now!** Get it here. **Where it stands.** On 25 SWE-bench-Live problems Tiel fixes 12 — the same as Opus 4.6 (medium), four more than Ornith-1.5 itself, three more than Nail, and four more than Sonnet 5 (medium). Among models of its own class it is first; the ones ahead are dense 27Bs and Opus 5. Its time per attempt is also steadier than Nail's: an 8.6 minute median against 7.2, but a 12.3 minute mean against 15.7, because it lacks Nail's tail of expensive attempts.

  • Ternary-Bonsai-2-4B-gguf↗

    prism-ml

    Prism ML Website  |  Demo & Examples  |  Discord A 4B-class vision-language model with ternary transformer weights, for llama.cpp (CUDA, Metal, CPU). - **Ternary language weights** across embeddings, attention projections, MLP projections and the (tied) LM head, stored with a group-wise FP16 scale. - **Three GGUF packings** of the same weights, consumed directly by custom kernels and never expanded back to FP16: **PTQ1_0** (dense trits, 0.95 GB), **PQ2_0** (2-bit slots, 1.15 GB) and **Q2_0** (1.21 GB). - **Vision input** through a separate mmproj pack (Q8_0, or BF16 for reference precision), loaded only when an image arrives. - **262K-token context**, inherited from the Qwen3.5-4B hybrid-attention backbone (24 linear-attention and 8 full-attention layers).

  • twitter-roberta-base-2022-154m↗

    cardiffnlp

    This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). A large model trained on the same data is available here. These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12. Filtering and preprocessing details are available in the TimeLMs paper. Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

  • wav2vec2-large-robust↗

    facebook

    The large model pretrained on 16kHz sampled speech audio. Speech datasets from multiple domains were used to pretrain the model: - Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data - CommonVoice: crowd-source collected audio data; read-out text snippets - Switchboard: telephone speech corpus; noisy telephone data - Fisher: conversational telephone speech; noisy telephone data **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli

  • SmolLM-360M↗

    HuggingFaceTB

    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.

  • DeepSeek-V4.1-Flash-1017-dsx-2bit↗

    lxe

    DeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) in 148 GB, built to run on two RTX 3090s and 128 GB of RAM with exact routing. Runs on **dsx**, a Rust + CUDA engine made for this model. For the best quality on the same machine, use dsx-ud (KL 0.269, one 305 GB download). All four run on dsx and were measured on the same eval (Lucebox 1017, 8 x 1,024 tokens against the FP8 model's top-256 log-probs). Unsloth's GGUFs were measured in llama.cpp with the same token ids and metric (tool). They also carry the Engram table, quantized to IQ4_NL (110.6 GB); dsx reads it unquantized from the official shards.

  • cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-q3c-groot16↗

    agurung

    Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical clean_eval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iid_canonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the clean_eval frontier eval). Reward signal: **binary code-correctness** (1.0 if the generated program passes the problem's tests, otherwise 0.0). This checkpoint is the **`main`** revision (git branch) of the repo, with the model at the repo root — load it directly, no subfolder:

  • MOSS-Image-Base-8-steps-dmd↗

    lodestones

    Work-in-progress distillation of OpenMOSS-Team/MOSS-Image-Base (50 steps, CFG 3) to 8 network evaluations, no CFG. Data-space DMD (arXiv 2610.12102): the generator iterates on its own clean estimate from fixed noise. Trained on compact-JSON `scene_graph` captions. Load `lora.safetensors` on the stock MOSS-Image-Base transformer (e.g. a ComfyUI LoRA loader, strength 1) and sample with the model's usual flow-matching Euler sampler, shift 5, **CFG 1**, 4-8 steps. Nothing else from this repo is needed. `transformer/` is a stock `MOSSImageTransformer` (load it with the release `pipeline_moss_image.py`); the text encoder, tokenizer and VAE are the base repo's.

  • Qwen-Image-2.1-Turbo-mflux-4bit↗

    OsaurusAI

    An Osaurus 4-bit mflux quantization of Qwen/Qwen-Image-2.1-Turbo for Apple Silicon (9.9 GB, smallest; more composition drift between seeds). This is not an official Qwen release and is not endorsed by the Qwen team. Turbo is the 8-step distilled checkpoint of Qwen-Image-2.1. One bundle does both **text-to-image** and **reference editing** (up to 10 ordered reference images); the vision encoder is included. Requires **Osaurus 0.25.21 or newer**. Older versions treat this bundle as Qwen-Image-2.1 and sample it on that model's 40-step schedule, which gives degraded images.