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AI 模型 85003

• Heejindo_-_rationale_model_e3_save5000_f2-gguf↗

  RichardErkhov

• ViraIntelligentDataMining_-_PersianLLaMA-13B-Instruct-gguf↗

  RichardErkhov

• ArmoRM-Llama3-8B-v0.1↗

  RLHFlow

  + **Blog**: https://rlhflow.github.io/posts/2024-05-29-multi-objective-reward-modeling/ + **Tech Report**: https://arxiv.org/abs/2406.12845 + **Model**: ArmoRM-Llama3-8B-v0.1 + Finetuned from model: FsfairX-LLaMA3-RM-v0.1 - **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/ + **Architecture** The second entry, "Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work.

• minimax_h3_ref2va_fp8_e4m3fn↗

  rzgar

  Quantized versions of the **MiniMax H3 Ref2VA** (reference-to-video-audio) diffusion model. Both keep the text-conditioning path (`token_refiner`, conditioning projections, audio heads) in full precision. _minimal 5 step (talking heads, less motions) | balanced 8-steps (shift 8) | Action-rich scenes above 12 (shift max 12)_

• Irodori-TTS-v4.1-Small-MF-GGUF↗

  sakasegawa

• codet5p-220m↗

  Salesforce

  CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. It is introduced in the paper: CodeT5+: Open Code Large Language Models for Code Understanding and Generation by Yue Wang\*, Hung Le\*, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi (* indicates equal contribution). Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as CodeGen to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following Code Alpaca.

• Multilingual-TTS-1.7B-Base↗

  Scicom-intl

• Realistic_Vision_V4.0_noVAE↗

  SG161222

  Check my exclusive models on Mage: ParagonXL / NovaXL / NovaXL Lightning / NovaXL V2 / NovaXL Pony / NovaXL Pony Lightning / RealDreamXL / RealDreamXL Lightning This model is available on Mage.Space (main sponsor) You can support me directly on Boosty - https://boosty.to/sg_161222 (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation

• calorie-lens↗

  shalev396

  A photo of a meal goes in; out come **the ingredients on the plate, how many grams of each, and the nutrition of every ingredient and of the whole plate** (kcal, protein, fat, carbs, fiber, sugars, saturated fat, sodium). It is two models fused into one network, trained on the Nutrition5k dishes, whose every ingredient was weighed on a scale, on plates weighed at a Finnish lunch line (FlavoriaFoodWeight1700) and on USDA FoodData Central. `config.json` records `pretrained: false`; `model.load()` rebuilds every network from `config.json` and loads `model.safetensors`, so inference never downloads a base model. The weights load with `transformers` versions before and after 5.18, which renamed DINOv2's attention layers. **Space API (free):** the Space exposes `/predict` (photo + optional total grams in, `[result, seconds, device]` out).

• Qwen3.8-27B-TURBO-Fable-Cold-Fusion-NEO-CODER-mlx-oQ8e-1M↗

  Solstice-AI