This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
This model is a fine-tuned version of Panga-Azazia/LFM2.5-350M-TTS-v3. It has been trained using TRL. This model was trained with SFT.
DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~380–400 tokens/sec on consumer GPUs like RTX 3090, and ~90 tokens/sec on legacy GTX 1070 laptops) while fitting in under 6 GB VRAM with Q4KM quantization. 1. Native Hermes Agentic Loop: - Explicit... deliberation before every action. - Structured... containers…
DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2 is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~360 tokens/sec on RTX 4080 Super / 3090, and ~80–90 tokens/sec on legacy mobile GPUs like the GTX 1070) while running in under 6 GB VRAM with Q4KM quantization. In our preliminary v1 release, an assistant role delimiter mismatch during training collation…
This repository contains official GGUF quantizations for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2, an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Because LFM2.5 activates only 1.5 billion parameters per token (out of 8.3B total parameters), inference speeds are extraordinarily high even on edge devices. 1. Search for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF directly inside LM Studio. 2. Select and download LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-Q4KM.gguf. 3. Load the model with GPU offload set to Max and context length set to 2048 or 4096 (ensure Flash Attention / KV Cache Q4 is…
This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2. - LoRA Rank ($r$): 64 - LoRA Alpha ($\alpha$): 128 Architected, fine-tuned, and evaluated with passion and neuro-symbiotic precision by DuoNeural: - Aura (Lead AI Cognitive Architect & Engineering Intelligence) - Archon (Claude-based Research Co-Architect & Theoretical Lead) - Jesse (Founder, Systems Engineer & AI/ML Researcher)
DuoNeural Systems & Production Quantization Architecture | Daedalus Protocol Produced by Jesse Caldwell, Archon, Aura (DuoNeural Research Lab) This repository presents the official TAP-DPQ v7 (Thouless-Anderson-Palmer Driven-Dissipative Post-Training Quantization v7) release of Liquid AI's LFM-2.5-8B-A1B. LFM-2.5-8B-A1B is a state-of-the-art hybrid architecture combining 18 Dynamic Convolution layers (shortconv with cache length $L=3$), 6 Multi-Head Self-Attention anchor layers, and 22 Mixture-of-Experts (MoE) layers with 32 experts (top-4 active routing). 1. Hurwitz Recurrent Core Stability: - The dynamic convolution temporal filtering kernels ($3 \times 2048$ per layer) govern the…
weighted/imatrix quants of https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/LFM2.5-Encoder-350M-Diffusion-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
basemodel: - sentence-transformers/LaBSE - language-identification - langID - sentence-embedding - similarity-search - low-resource - low-resource-languages
We introduce LIFT, a unified image-to-video generation framework that complements camera control with Layout-In-FuTure control, enabling users to specify what should appear in a future view and where it should appear. Our models are built on Wan2.1-Fun-V1.1-1.3B-Control-Camera.
Every weight in this model was produced by this project. There is no frompretrained, no base checkpoint, and no adapter: the tokenizer, the architecture code, the data pipeline and the training loop are all bespoke, and training started from a random initialisation. Trained on a single RTX 5070 Ti (16 GB). Written by hand — no transformers model classes were used to define it. 309,705,984 transformer blocks. Bits per byte is the headline metric. This model's vocabulary (32,768) differs from GPT-2's (50,257), and cross-entropy per token is not comparable across tokenizers. BPB divides that out and is directly comparable. Validation loss is measured on a held-out slice of the training corpus…
LoRA adapters fine-tuning Qwen2.5-7B-Instruct to emit a full CIF crystal structure from a prompt of reduced composition + target space-group number. Part of a controlled composition-sweep study (MP-20: MPTS-52 training ratio at fixed volume/steps). Trained with Unsloth, LoRA r=32 / α=64, lr 1e-4, 24,000 unique training crystals, pinned 4,500 steps (no early stopping), 16-bit. Adapter base: unsloth/Qwen2.5-7B-Instruct (loads fine on stock Qwen/Qwen2.5-7B-Instruct). Finding: at matched volume + steps, match declines monotonically as the MP-20 fraction rises — the gain reported for "combined" data was volume, not symmetry composition. Each subfolder contains: model/ (LoRA adapter +…
A 97.6M-parameter language model trained from scratch, then fine-tuned for short, polite, everyday English conversation. It is a small, open research and learning model: you can read every line of its training code, run it on a laptop CPU, and see exactly where a model of this size is good and where it fails. - HellaSwag 33.6% (accnorm). That is above GPT-2 small (~30%) and below SmolLM2-135M (43.1%), which saw about 250x more training text. - The custom PyTorch code is included. The model does not use transformers; see How to use. This is QuickTalk run 8. "LMLM97M1" is its published name. 1. Learning and teaching how LLMs work. It is a complete, small, from-scratch GPT, with its training…
LT-OPD trains vision-language models with fewer visual tokens through on-policy self-distillation. This Qwen3.5-4B model combines CDPruner with an MLP that aggregates discarded tokens into their nearest retained tokens, preserving their original positions without adding tokens. Use Python 3.12 and the CUDA environment in the installation guide. Load the model with LT-OPD's visual-token compression runtime
LTX-2 dev LoRA optimized with AVC-Reward. From the GitHub repository, after installing the LTX-2 runtime and preparing the Gemma encoder
Cinematic behind-the-scenes movie sets for LTX-2.5. A LoRA by SOLRICKS for the atmosphere of high-budget film production: professional camera rigs, cranes, dolly tracks, studio lighting, green/blue screens, practical sets and working film crews. Supports both T2V and I2V generation with LTX-2.5. The released LoRA weights are available directly in this repository: Download BTSMovieSetLTX25v1.safetensors. A copy is also available on Civitai. 1. Download BTSMovieSetLTX25v1.safetensors from this model page. 2. Place the file in ComfyUI/models/loras/ and load it in your LTX-2.5 workflow. 3. Start with a LoRA weight of 0.8 and include btssetstyle in the positive prompt. 4. For T2V, describe the…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.