# Text Generation Models, Page 17: Open-Weight AI Source: https://savrn.com/models/tasks/text-generation/page/17 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by Task # Text Generation Models 1,582 open-weight text generation models in the SAVRN Model Hub, with Convergent Intelligence, Qwen and Francesca Padovani publishing the most. 1,582Models 732Publishers 107K to 1.7TParameter range 8Licenses ## SAVRN's Take Text generation is where most of the attention goes, and where most of the money is spent. The models on this page run from tiny research checkpoints that fit on a laptop to frontier-class systems that need a full server of accelerators. The first question we ask about any of them is not how smart it is, but what it costs to keep it answering around the clock. That cost comes down to three things: the size of the weights, the precision you run them at, and how much context each request carries. Moving from 16-bit to 4-bit cuts the memory a model needs by roughly four times, which is often the difference between one server and several. Mixture-of-experts models, such as the newer DeepSeek, Qwen and Nemotron releases, hold a large total parameter count but activate only a small share per token, so they answer faster than their size suggests while still needing the memory to hold every expert. Licensing decides the rest. Apache 2.0 and MIT models can go straight into a commercial product. Llama, Gemma's earlier generations and models marked other each carry terms you need to read first, and a model with no stated license is research material until the publisher says otherwise. Our advice is to shortlist by license, size to the hardware you can actually run, then test the survivors on your own prompts. The table below is sorted by what people download, not by what we recommend. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [Qwen3-0.6B](https://savrn.com/models/qwen3-0-6b) | Qwen | 752M | apache-2.0 | 25.2M | 1x MI300X, $1.85/hr | | [gpt2](https://savrn.com/models/gpt2) | OpenAI community | 137M | mit | 18.8M | 1x MI300X, $1.85/hr | | [Qwen3-4B](https://savrn.com/models/qwen3-4b) | Qwen | 4B | apache-2.0 | 11.4M | 1x MI300X, $1.85/hr | | [Qwen3-14B](https://savrn.com/models/qwen3-14b) | Qwen | 14.8B | apache-2.0 | 9.4M | 1x MI300X, $1.85/hr | | [Qwen3-8B](https://savrn.com/models/qwen3-8b) | Qwen | 8.2B | apache-2.0 | 8.1M | 1x MI300X, $1.85/hr | | [Qwen2.5-7B-Instruct](https://savrn.com/models/qwen2-5-7b-instruct) | Qwen | 7.6B | apache-2.0 | 6.4M | 1x MI300X, $1.85/hr | | [gpt-oss-20b](https://savrn.com/models/gpt-oss-20b) | OpenAI | 20.9B | apache-2.0 | 5.7M | 1x MI300X, $1.85/hr | | [opt-125m](https://savrn.com/models/opt-125m) | AI at Meta | — | other | 5.6M | — | | [DeepSeek-V3.2](https://savrn.com/models/deepseek-v3-2) | DeepSeek | 685.4B | mit | 5.4M | 7x MI325X, $14.00/hr | | [Qwen2.5-0.5B-Instruct](https://savrn.com/models/qwen2-5-0-5b-instruct) | Qwen | 494M | apache-2.0 | 5.3M | 1x MI300X, $1.85/hr | ## Licenses | License | Models | Commercial use | | --- | --- | --- | | [apache-2.0](https://savrn.com/models/licenses/apache-2-0) | 734 | Yes | | not stated | 345 | Not stated | | other | 175 | Read the license | | [mit](https://savrn.com/models/licenses/mit) | 152 | Yes | | [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) | 42 | Not without separate permission | | [llama3.2](https://savrn.com/models/licenses/llama3-2) | 33 | Read the license | | [gemma](https://savrn.com/models/licenses/gemma) | 22 | Yes, with conditions | | [llama3.1](https://savrn.com/models/licenses/llama3-1) | 21 | Yes, with conditions | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Convergent Intelligence](https://savrn.com/model-publishers/reaperdoesntknow) | 54 | | [Qwen](https://savrn.com/model-publishers/qwen) | 42 | | [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) | 37 | | [Yanggangu](https://savrn.com/model-publishers/yanggangu) | 28 | | [Shreyansh singh](https://savrn.com/model-publishers/shreyansh12183) | 25 | | [Kumar](https://savrn.com/model-publishers/sandeep123) | 23 | ## All 1,582 Models, Page 17 of 27 Model · Text generation ### [less-is-moe-qwen3.5-122b-a10b-gpqa-main-64-intdim-l-50](https://savrn.com/models/less-is-moe-qwen3-5-122b-a10b-gpqa-main-64-intdim-l-50) [XINKAI ZOU](https://savrn.com/model-publishers/jayzou3773) This checkpoint was structurally pruned with the released Less-is-MoE mean-absolute-gradient method. It removes exactly 50% of routed-expert FFN neurons using 64 calibration samples from the gpqamain configuration of Idavidrein/gpqa revision 633f5ee89ab8ad4522a9f850766b73f62147ffdd. The released loader settings are preserved: train, Question plus shuffled choices, Explanation, selectionseed=1234, BF16, and no optimizer step. The samples are full length: no tokenizer maxlength, truncation, or padding. The longest input for this tokenizer is 1,632 tokens. The source checkpoint was loaded and pruned in BF16. The source-row selection hash is… Open weights apache-2.0 64.1B parameters 262,144 tokens [View model](https://savrn.com/models/less-is-moe-qwen3-5-122b-a10b-gpqa-main-64-intdim-l-50) Model · Text generation ### [LFM2.5-24B-A2B-DERISKED-GGUF](https://savrn.com/models/lfm2-5-24b-a2b-derisked-gguf) [Jackson](https://savrn.com/model-publishers/freakyskittle) GGUF quants of Blackfrost-AI/LFM2.5-24B-A2B-DERISKED-BF16. Text model. This checkpoint has no vision tower and no mmproj. Architecture is Lfm2MoeForCausalLM (lfm2moe). About 24B parameters, about 2.45B active. 40 blocks: 2 dense, 38 MoE. 64 experts, 4 routed per token. 30 short-conv blocks and 10 attention blocks. Tied embeddings. Configured context is 131072. No MTP / NextN tensors in the source checkpoint. The source is a behaviorally modified BF16 derivative of LiquidAI/LFM2.5-24B-A2B. "DERISKED" is the repository name, not a safety certification. The parent license still applies. See LICENSE. Quantized with llama.cpp llama-quantize from a BF16 GGUF. No importance matrix. Embeddings and… Open weights other llama.cpp [View model](https://savrn.com/models/lfm2-5-24b-a2b-derisked-gguf) Model · Text generation ### [LFM2.5-24B-A2B-GGUF](https://savrn.com/models/lfm2-5-24b-a2b-gguf) [Nguyen Quoc Vinh](https://savrn.com/model-publishers/ngquocvinh) Community GGUF quantizations of LiquidAI/LFM2.5-24B-A2B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. Modification notice: The GGUF files in this repository are community-modified derivatives of the upstream BF16 checkpoint, converted to GGUF and quantized by this publisher. LFM2.5-24B-A2B is Liquid AI's text-only reasoning Mixture-of-Experts model. It has 24 billion total parameters and 2.45 billion active parameters, with 40 layers, 64 experts per MoE block, top-4 routing, and a 131,072-token context window. The upstream card lists English, Arabic, Chinese, French… Open weights other llama.cpp [View model](https://savrn.com/models/lfm2-5-24b-a2b-gguf) Model · Text generation ### [LFM2.5-24B-A2B-JANGH2](https://savrn.com/models/lfm2-5-24b-a2b-jangh2) [Osaurus-AI](https://savrn.com/model-publishers/osaurusai) A JANG bundle of LiquidAI/LFM2.5-24B-A2B: Liquid AI's 24B mixture-of-experts reasoning model with 2B active parameters (40 layers: 30 short-convolution and 10 full-attention, 64 routed experts with 4 active, two dense layers), built for on-device agents. It always thinks before answering and calls tools in Liquid's Pythonic format. 44.7 GiB of bf16 weights in 7.3 GiB. Every quantized matrix with an input Hessian was rounded with GPTQ. The Hessians come from the model's own outputs: the bf16 model wrote its thinking and answers for math, finance, code in eleven languages, terminal and coding-agent sessions (with real tool results), multi-step tool calls, networking, cybersecurity, science… Open weights apache-2.0 2.7B parameters 131,072 tokens mlx [View model](https://savrn.com/models/lfm2-5-24b-a2b-jangh2) Model · Text generation ### [LFM2.5-24B-A2B-OptiQ-4bit](https://savrn.com/models/lfm2-5-24b-a2b-optiq-4bit) [MLX Community](https://savrn.com/model-publishers/mlx-community) An OptiQ mixed-precision quant of LiquidAI/LFM2.5-24B-A2B, the largest model in the LFM2.5 family. It is 16.7 GB on disk, down from 47.9 GB at bf16, with a 128k context. LFM2.5-24B-A2B is a sparse mixture of experts on Liquid's hybrid backbone. Convolutional blocks alternate with full attention, so only 10 of its 40 layers keep a KV cache. Each token is routed to 4 of 64 experts. OptiQ measures each layer's sensitivity and assigns its bit-width from that. Most of the routed expert weights sit at 4-bit: 87 of the 114 fused expert projections. Attention, the routers and nearly all of the convolution blocks stay at 8-bit. Six-metric mean, the standard OptiQ eval, run in reasoning mode because… Open weights other 24B parameters 131,072 tokens mlx [View model](https://savrn.com/models/lfm2-5-24b-a2b-optiq-4bit) Model · Text generation ### [LFM2.5-24B-A2B-Turbo-Brilliance-50X-NEO-MAX-Imatrix-GGUF](https://savrn.com/models/lfm2-5-24b-a2b-turbo-brilliance-50x-neo-max-imatrix-gguf) [David Belton](https://savrn.com/model-publishers/davidau) generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning (27) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, and our fine tunes too). The Turbo Brilliance system (27 reasoning modes) vastly augments the models core performance and includes interactive help system directly connected to the model. This is like playing "Pong" vs "Entering the Matrix"... because Turbo… Open weights other [View model](https://savrn.com/models/lfm2-5-24b-a2b-turbo-brilliance-50x-neo-max-imatrix-gguf) Model · Text generation ### [LFM2.5-350M-TTS-v3](https://savrn.com/models/lfm2-5-350m-tts-v3) [Kim](https://savrn.com/model-publishers/panga-azazia) This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT. Access requested at publisher 383M parameters transformers [View model](https://savrn.com/models/lfm2-5-350m-tts-v3) Model · Text generation ### [LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated) [DuoNeural](https://savrn.com/model-publishers/duoneural) 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… Open weights other 8.5B parameters 128,000 tokens hermes [View model](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated) Model · Text generation ### [LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2) [DuoNeural](https://savrn.com/model-publishers/duoneural) 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… Open weights other 8.5B parameters 128,000 tokens hermes [View model](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2) Model · Text generation ### [LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) 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… Open weights other hermes [View model](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2-gguf) Model · Text generation ### [LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-LoRA](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2-lora) [DuoNeural](https://savrn.com/model-publishers/duoneural) 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) Open weights other peft [View model](https://savrn.com/models/lfm2-5-8b-a1b-hermes-agentic-coder-abliterated-v2-lora) Model · Text generation ### [LFM2.5-8B-A1B-TAP-DPQ-v7-GGUF](https://savrn.com/models/lfm2-5-8b-a1b-tap-dpq-v7-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) 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… Open weights apache-2.0 [View model](https://savrn.com/models/lfm2-5-8b-a1b-tap-dpq-v7-gguf) Model · Text generation ### [LibraMindMini](https://savrn.com/models/libramindmini) [Alby13](https://savrn.com/model-publishers/alby13) LibraMind is a 565M-parameter chat model pretrained from scratch by alby13 on a single consumer GPU (NVIDIA RTX 4090). It is a hybrid architecture that interleaves 18 Gated DeltaNet linear-recurrent layers with 6 Gated Attention layers (a repeating DDDA × 6 pattern), with Block Attention Residuals and logit soft-capping, trained with the Muon optimizer. It went through a full five-stage pipeline: pretraining, midtraining, supervised fine-tuning, preference tuning, and RL with verifiable rewards. - Research on small hybrid recurrent/attention language models - Studying low-resource, single-GPU training pipelines (pretraining through RL) and the Muon optimizer at small scale - Experiments… Open weights apache-2.0 565M parameters pytorch [View model](https://savrn.com/models/libramindmini) Model · Text generation ### [lightning-105m](https://savrn.com/models/lightning-105m) [AobanZ](https://savrn.com/model-publishers/aobangaming) Lightning is a small, autoregressive transformer which utilizes FlashAttention and MHA. This model is trained on a variety of books from a dataset(300 MB). This is the expanded version of Lightning-60m. Lightning utilizes FlashAttention and AdamW for performance and capability. Lightning is designed to provide quick, coherent outputs, improved with a larger size and weight. Lightning is intended to be used for research, analysis and fine-tuning, stories and other. It is not intended to be used for professional advice, real writing or any kind of heavy work as generated outputs may be incorrect. Lightning can be used directly for text generation, experimentation, and conversational… Open weights mit 105M parameters transformers [View model](https://savrn.com/models/lightning-105m) Model · Text generation ### [Ling-1T-GGUF](https://savrn.com/models/ling-1t-gguf) [Mohannad Amr](https://savrn.com/model-publishers/honda6666) This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! NOTE ikllama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8. These quants provide best in class perplexity for the given memory footprint. Shout out to Wendell and the Level1Techs crew, the… Open weights mit [View model](https://savrn.com/models/ling-1t-gguf) Model · Text generation ### [Llama-3.1-8B-Instruct-Conceptizer](https://savrn.com/models/llama-3-1-8b-instruct-conceptizer) [Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla) Open weights llama3.1 peft [View model](https://savrn.com/models/llama-3-1-8b-instruct-conceptizer) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-20Minuten](https://savrn.com/models/llama-3-1-8b-instruct-ft-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-20minuten) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-C-STANCE](https://savrn.com/models/llama-3-1-8b-instruct-ft-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-c-stance) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-FOMC](https://savrn.com/models/llama-3-1-8b-instruct-ft-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-fomc) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-MeetingBank](https://savrn.com/models/llama-3-1-8b-instruct-ft-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-meetingbank) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-NumGLUE-cm](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-cm) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-NumGLUE-ds](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-ds) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-ScienceQA](https://savrn.com/models/llama-3-1-8b-instruct-ft-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-scienceqa) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-20Minuten](https://savrn.com/models/llama-3-1-8b-instruct-smat-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-20minuten) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-C-STANCE](https://savrn.com/models/llama-3-1-8b-instruct-smat-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-c-stance) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-FOMC](https://savrn.com/models/llama-3-1-8b-instruct-smat-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-fomc) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-MeetingBank](https://savrn.com/models/llama-3-1-8b-instruct-smat-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-meetingbank) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-NumGLUE-cm](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-cm) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-NumGLUE-ds](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-ds) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-ScienceQA](https://savrn.com/models/llama-3-1-8b-instruct-smat-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-scienceqa) Model · Text generation ### [Llama-3.1-8B-Reponx-Network-Cloud-Ops](https://savrn.com/models/llama-3-1-8b-reponx-network-cloud-ops) [Reponx](https://savrn.com/model-publishers/reponx) Built with Llama. A LoRA adapter for meta-llama/Llama-3.1-8B-Instruct that turns it into a network and cloud operations assistant for Cisco, Palo Alto Networks, Microsoft Azure and AWS. It is trained to work with retrieved reference documents (RAG): it diagnoses the problem, rates the risk of the change, requires approval, and takes the exact commands from the references you provide instead of from memory. When the references do not contain the needed commands, it says so and does not guess. Diagnosis → Risk → Action Type (read / write / high) → Approval → Warning → Commands/Action → Verification → Rollback Held-out test of 124 questions (never seen in training), with the correct reference… Open weights llama3.1 peft [View model](https://savrn.com/models/llama-3-1-8b-reponx-network-cloud-ops) Model · Text generation ### [Llama-3.2-1B-Instruct-Conceptizer](https://savrn.com/models/llama-3-2-1b-instruct-conceptizer) [Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla) Open weights llama3.2 peft [View model](https://savrn.com/models/llama-3-2-1b-instruct-conceptizer) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-20Minuten](https://savrn.com/models/llama-3-2-1b-instruct-ft-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-20minuten) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-C-STANCE](https://savrn.com/models/llama-3-2-1b-instruct-ft-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-c-stance) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-FOMC](https://savrn.com/models/llama-3-2-1b-instruct-ft-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-fomc) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-MeetingBank](https://savrn.com/models/llama-3-2-1b-instruct-ft-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-meetingbank) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-NumGLUE-cm](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-cm) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-NumGLUE-ds](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-ds) Model · Text generation ### [Llama-3.2-1B-Instruct-FT-ScienceQA](https://savrn.com/models/llama-3-2-1b-instruct-ft-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-scienceqa) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-20Minuten](https://savrn.com/models/llama-3-2-1b-instruct-smat-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-20minuten) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-C-STANCE](https://savrn.com/models/llama-3-2-1b-instruct-smat-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-c-stance) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-FOMC](https://savrn.com/models/llama-3-2-1b-instruct-smat-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-fomc) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-MeetingBank](https://savrn.com/models/llama-3-2-1b-instruct-smat-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-meetingbank) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-NumGLUE-cm](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-cm) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-NumGLUE-ds](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-ds) Model · Text generation ### [Llama-3.2-1B-Instruct-SMAT-ScienceQA](https://savrn.com/models/llama-3-2-1b-instruct-smat-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply. Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-scienceqa) Model · Text generation ### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff) [Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23) open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. TOFU contains 200 fictitious authors with 20 QA pairs each. forget10 holds 10% of them (20 authors, 400 QA pairs); retain90 holds the other 180 authors. The oracle is the same base model fine-tuned on retain90 only, i.e. a model that never saw the forget set. Hyperparameter… Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff) Model · Text generation ### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff-ILU](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-ilu) [Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23) open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff + ILU. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. ILU (Invariant LLM Unlearning; Wang et al., ICML 2025) is applied on top of GradDiff as a relearning defense: adds an IRMv1 invariance penalty computed on an unrelated task (GSM8K) so that the unlearning solution stays stationary under fine-tuning on other data (reimplemented… Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-ilu) Model · Text generation ### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff-SAM](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-sam) [Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23) open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff + SAM. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. SAM (Sharpness-Aware Minimization for robust unlearning; Fan et al., ICML 2025) is applied on top of GradDiff as a relearning defense: the forget loss is evaluated at the sharpness-aware perturbed weights $\theta + \rho\,g/\lVert g\rVert$ (retain gradient at $\theta$), so the… Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-sam) Model · Text generation ### [Llama-3.2-1B-Instruct-TOFU-forget10-NPO](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-npo) [Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23) open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with NPO. NPO (Negative Preference Optimization; Zhang et al., 2024): $\mathcal{L} = \gamma\,\tfrac{2}{\beta}\,\mathbb{E}{\text{forget}}\big[\log\big(1+(\pi\theta/\pi{\text{ref}})^{\beta}\big)\big] + \alpha\,\mathrm{NLL}(\text{retain})$, with $\pi{\text{ref}}$ the frozen starting model. TOFU contains 200 fictitious authors with 20 QA pairs each. forget10 holds 10% of them (20 authors, 400 QA pairs); retain90 holds the other 180 authors. The oracle is the same base model fine-tuned on retain90 only, i.e. a model that never saw… Open weights llama3.2 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-npo) Model · Text generation ### [Llama-3.2-3B-Instruct-Conceptizer](https://savrn.com/models/llama-3-2-3b-instruct-conceptizer) [Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla) Open weights llama3.2 peft [View model](https://savrn.com/models/llama-3-2-3b-instruct-conceptizer) Model · Text generation ### [Llama-3.2-3B-Instruct-Profesor-Albus-Dumbledore](https://savrn.com/models/llama-3-2-3b-instruct-profesor-albus-dumbledore) [Nicole Rosas](https://savrn.com/model-publishers/rrnicole1014) This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 3.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-3b-instruct-profesor-albus-dumbledore) Model · Text generation ### [Llama-3.2-3B-Legal-SFT](https://savrn.com/models/llama-3-2-3b-legal-sft) [Muhammad Idris](https://savrn.com/model-publishers/midris) This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 3.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-2-3b-legal-sft) Model · Text generation ### [Llama-3.2-3B-RAG-Adapter](https://savrn.com/models/llama-3-2-3b-rag-adapter) [K Jeshwanth](https://savrn.com/model-publishers/the1geggbwb) Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.21.0 Open weights peft [View model](https://savrn.com/models/llama-3-2-3b-rag-adapter) Model · Text generation ### [Llama-3.2-3B-SPARQL-GGUF](https://savrn.com/models/llama-3-2-3b-sparql-gguf) [Duban Potosi](https://savrn.com/model-publishers/dubansmith) Built with Llama Modelo basado en meta-llama/Llama-3.2-3B-Instruct, ajustado con LoRA para generar consultas SPARQL. Modelo base con el adaptador LoRA: Describe aquí tu dataset, hiperparámetros y métricas. Distribuido bajo la Llama 3.2 Community License. Ver LICENSE y NOTICE. Uso sujeto a la Acceptable Use Policy de Meta. Open weights llama3.2 [View model](https://savrn.com/models/llama-3-2-3b-sparql-gguf) Model · Text generation ### [Llama-3.2-3B-Thai_CRM](https://savrn.com/models/llama-3-2-3b-thai-crm) [Yothin](https://savrn.com/model-publishers/yothins) Fine-tuned version of meta-llama/Llama-3.2-3B optimized for Customer Relationship Management (CRM), Customer Behavior Analysis, and Thai business workflows. Trained using Unsloth and Hugging Face's TRL library. from transformers import AutoModelForCausalLM, AutoTokenizer import torch modelid = "yothinS/Llama-3.2-3B-ThaiCRM" tokenizer = AutoTokenizer.frompretrained(modelid) model = AutoModelForCausalLM.frompretrained( modelid, torchdtype=torch.bfloat16, devicemap="auto" messages = [ {"role": "system", "content": "You are a professional CRM and customer behavior specialist."}, {"role": "user", "content": "วิเคราะห์พฤติกรรมลูกค้ารายนี้และแนะนำโปรโมชันรักษาฐานลูกค้า: ยอดซื้อเฉลี่ยลดลง 40%… Open weights llama3.2 3.2B parameters 131,072 tokens [View model](https://savrn.com/models/llama-3-2-3b-thai-crm) Model · Text generation ### [Llama-3.2-3B-Thai-CRM-GGUF](https://savrn.com/models/llama-3-2-3b-thai-crm-gguf) [Yothin](https://savrn.com/model-publishers/yothins) This repository contains GGUF quantized weights for yothinS/Llama-3.2-3B-ThaiCRM, a model fine-tuned for Customer Relationship Management (CRM) workflows, Thai business communications, and customer behavioral analysis. GGUF files can be run locally on CPU, Apple Silicon, or NVIDIA/AMD GPUs via llama.cpp, Ollama, LM Studio, or carrany/text-generation-webui. Download your chosen GGUF file using the Hugging Face CLI: huggingface-cli download yothinS/Llama-3.2-3B-ThaiCRM-GGUF Llama-3.2-3B-ThaiCRM.Q4KM.gguf --local-dir. Open weights llama3.2 [View model](https://savrn.com/models/llama-3-2-3b-thai-crm-gguf) Model · Text generation ### [LLM_Crystal_CIF](https://savrn.com/models/llm-crystal-cif) [Shehroz Ahmad](https://savrn.com/model-publishers/shehrozashoaib) 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 +… Open weights apache-2.0 peft [View model](https://savrn.com/models/llm-crystal-cif) Model · Text generation ### [llm-demo](https://savrn.com/models/llm-demo) [Jor Teron](https://savrn.com/model-publishers/jor-t) Open weights transformers [View model](https://savrn.com/models/llm-demo) Model · Text generation ### [llm-jp-4-8b-thinking_imabari_qa_v4_reasoning_effort_grpo_v5](https://savrn.com/models/llm-jp-4-8b-thinking-imabari-qa-v4-reasoning-effort-grpo-v5) [Ikedachin](https://savrn.com/model-publishers/ikedachin) Japanese description is available below. This model is a fully merged model created by applying GRPO training to the following supervised fine-tuned model: The immediate base model was fine-tuned on QA data containing both Imabari dialect and standard Japanese reasoning and answers. This v5 model applies additional GRPO training intended to: 1. Improve compliance with the expected reasoning-to-final-answer channel format 3. Preserve support for standard Japanese and Imabari-dialect channels 4. Preserve the low, medium, and high reasoning-effort conditions The GRPO reward used in this training run does not evaluate factual correctness, answer relevance, or dialect naturalness. It evaluates… Open weights other 8.6B parameters 65,536 tokens transformers [View model](https://savrn.com/models/llm-jp-4-8b-thinking-imabari-qa-v4-reasoning-effort-grpo-v5) ## Questions ### Which Text generation models are most downloaded? By monthly downloads reported by the Hugging Face Hub: . ## Other Tasks - [Image and Text to Text](https://savrn.com/models/tasks/image-and-text-to-text) 588 - [Robotics](https://savrn.com/models/tasks/robotics) 403 - [Feature Extraction](https://savrn.com/models/tasks/feature-extraction) 400 - [Text Classification](https://savrn.com/models/tasks/text-classification) 335 - [Text to Image](https://savrn.com/models/tasks/text-to-image) 236 - [Speech Recognition](https://savrn.com/models/tasks/speech-recognition) 190 - [Text to Speech](https://savrn.com/models/tasks/text-to-speech) 148 - [Image Classification](https://savrn.com/models/tasks/image-classification) 140 - [Audio Classification](https://savrn.com/models/tasks/audio-classification) 130 - [Sentence Similarity](https://savrn.com/models/tasks/sentence-similarity) 103 - [Reinforcement Learning](https://savrn.com/models/tasks/reinforcement-learning) 102 - [Token Classification](https://savrn.com/models/tasks/token-classification) 101 [See all](https://savrn.com/models/tasks)