全站搜索
AI 模型 2119
- vakyansh-wav2vec2-tamil-tam-250↗
Harveenchadha
Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz. **Note: The result from this model is without a language model so you may witness a higher WER in some cases.** This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
- SambaLingo-Thai-Chat-70B-i1-GGUF↗
mradermacher
- MOSS-Image-SFT-4-steps↗
akhaliq
> ⚡ **Demo:** MOSS-Image 4-Step Turbo Space > 🆕 **v2.0 (2026-10-18) — major update, usage changed.** The model is now > **Euler-native**: a single `lora.safetensors` that runs on the **stock > flow-matching Euler sampler** (any LoRA loader, 4 steps, CFG 1) — the custom > data-space sampler and the `time_embedding.safetensors` / > `dataspace.safetensors` files are **no longer needed and were removed from > the repo root**. If you used v1.x adapters, grab the old data-space variant > from `checkpoint-update-400/` (it keeps its own sampler), or switch to the > new one-line loader below. New users: just follow the Quickstart. The fastest high-fidelity MOSS-Image-SFT: **4 steps, no CFG, one LoRA file, stock sampler.** No custom loop, no auxiliary safetensors, no pipeline patches — it drops into any standard LoRA loader.
- Qwen3.8-35B-A3B-Distill-GGUF↗
empero-ai
GGUF quantizations of **empero-ai/Qwen3.8-35B-A3B-Distill** — a distillation of the Qwen3.8 frontier models into the Qwen3.6-35B-A3B Mixture-of-Experts architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes. This card is about choosing a file and running it. The capability writeup, benchmark results, and best practices live on the **main model card**. 35B total parameters with ~3B active per token — the MoE sparsity means it runs considerably faster than a dense 35B at the same quant, but the **whole weight file still has to fit in RAM or VRAM**.
- sam3.1↗
facebook
- wav2vec2-large-xlsr-malayalam↗
gvs
Fine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file. A combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook
- Latxa-Llama-3.1-8B-Instruct↗
HiTZ
We introduce Latxa 3.1 8B Instruct, an instructed version of Latxa. This new Latxa is based on Llama-3.1 (Instruct), which we trained on our Basque corpus (Etxaniz et al., 2024) comprising 4.3M documents and 4.2B tokens using language adaptation techniques (paper in preparation). > [!WARNING] > DISCLAIMER > > This model is still under development. > Further training details will be released with the corresponding research paper in the near future. Our preliminary experimentation shows that Latxa 3.1 8B Instruct outperforms Llama-3.1-Instruct by a large margin on Basque standard benchmarks, and particularly, on chat conversations. In addition, we organized a public arena-based evaluation, on which Latxat competed against other baselines and proprietary models such as GPT-4o and Claude Sonnet. The results showed that Latxa ranked 3rd, just behind Claude and GPT-4 and above all the other same-size competitors. The official paper is coming soon. Latxa is a family of Large Language Models (LLM) based on Meta’s LLaMA models. Current LLMs exhibit incredible performance for high-resource languages such as English, but, in the case of Basque and other low-resource languages, their performance is close to a random guesser. These limitations widen the gap between high- and low-resource languages when it comes to digital development. We present Latxa to overcome these limitations and promote the development of LLM-based technology and research for the Basque language. Latxa models follow the same architecture as their original counterparts and were further trained in Latxa Corpus v1.1, a high-quality Basque corpora.
- Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-4.7bpw↗
ddalcu
mlx-serve pack of Qwen/Qwen3.8-Flash-Next, the Qwen4 preview architecture (`model_type: qwen4_exp`). Runs on a 128 GB Mac with about 75 GB resident. Includes the MTP head and the vision tower (image and video input). This is the calibrated twin of Qwen3.8-Flash-Next-MLX-Serve-mixed-4-8bit: the same layout and the same size, byte for byte (every expert 4-bit group 64, the rest 8-bit), but every expert is quantized with an importance matrix collected on the bf16 model, so the scales follow the activations the weights actually see. Same speed, same memory, closer to bf16. The importance matrix was collected on the bf16 checkpoint over 1.35 M tokens of agent transcripts, code (including SWE-bench Lite issues and patches), prose and math, every expert of every layer hit. Quality is scored against the bf16 model's own logits on 290 held-out positions (top-1: the pack's greedy token equals bf16's; KLD: KL(bf16 || pack) over bf16's top-1024 tokens; the KLD mean is dominated by a few flat prose positions, so the median is the steadier number):
- Qwen3.5-4B-RKLLM↗
HanzoHuang
Use the files for your board’s exact SoC. `w4a16` uses 4-bit weights and 16-bit activations; `w4a16_g128` adds a weight group size of 128. `w8a8` uses 8-bit weights and activations. File size below is download size, not total runtime RAM. For image input, also download the **vision encoder for the same SoC**. Keep the language model and vision encoder from this release together. With Docker installed on a matching Rockchip board, this example starts the **RK3576 w4a16_g128** model and its API on port 8001:
- dscript_human_v1↗
samsl