# Text Generation Models, Page 19: Open-Weight AI Source: https://savrn.com/models/tasks/text-generation/page/19 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, $1.85/hr | ## Licenses | License | Models | Commercial use | | --- | --- | --- | | [apache-2.0](https://savrn.com/models/licenses/apache-2-0) | 734 | Yes | | not stated | 345 | Not stated | | other | 175 | Read the license | | [mit](https://savrn.com/models/licenses/mit) | 152 | Yes | | [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) | 42 | Not without separate permission | | [llama3.2](https://savrn.com/models/licenses/llama3-2) | 33 | Read the license | | [gemma](https://savrn.com/models/licenses/gemma) | 22 | Yes, with conditions | | [llama3.1](https://savrn.com/models/licenses/llama3-1) | 21 | Yes, with conditions | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Convergent Intelligence](https://savrn.com/model-publishers/reaperdoesntknow) | 54 | | [Qwen](https://savrn.com/model-publishers/qwen) | 42 | | [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) | 37 | | [Yanggangu](https://savrn.com/model-publishers/yanggangu) | 28 | | [Shreyansh singh](https://savrn.com/model-publishers/shreyansh12183) | 25 | | [Kumar](https://savrn.com/model-publishers/sandeep123) | 23 | ## All 1,582 Models, Page 19 of 27 Model · Text generation ### [nebium-small](https://savrn.com/models/nebium-small) [Nabin Oli](https://savrn.com/model-publishers/nabin2004) Nebium-Small is a 117-million-parameter causal Transformer trained for autoregressive next-chess-move prediction over Lichess UCI move sequences. - Rotary Position Embeddings (RoPE) on attention query and key projections ($\theta = 10000$) - SwiGLU feed-forward transformation - RMSNorm pre-normalization - Causal mask with padding token masking - Byte-Pair Encoding (BPE) tokenizer trained on UCI move plies $$L(N, D) = 1.69 + \frac{406.4}{N^{0.34}} + \frac{410.7}{D^{0.28}}$$ MIT License. Open weights mit pytorch [View model](https://savrn.com/models/nebium-small) Model · Text generation ### [nebium-small-gguf](https://savrn.com/models/nebium-small-gguf) [Nabin Oli](https://savrn.com/model-publishers/nabin2004) Quantized and FP16 GGUF format binaries for Nebium-Small (117M parameters). Designed for low-latency CPU and GPU execution with llama.cpp and Ollama. MIT License. Open weights mit [View model](https://savrn.com/models/nebium-small-gguf) Model · Text generation ### [Nemotron-3.5-Bolt-74B-A9B-CRACK-24GB-GGUF](https://savrn.com/models/nemotron-3-5-bolt-74b-a9b-crack-24gb-gguf) [Dealign.ai](https://savrn.com/model-publishers/dealignai) NemotronH hybrid Mamba2 + attention + MoE · 74B total / 9B active · VL (image + video) · 22 GB on disk This is NVIDIA's own Nemotron-3.5-Bolt-74B-A9B with its refusal training removed and quantized to NVIDIA's own 24GB mixed-precision layout. The multimodal vision tower is kept byte-identical to the BF16 source; it is shipped with NVIDIA's own mmproj file and the matching LiLiCorr speculative drafter. llama.cpp upstream b76b13379 or newer. Older forks cannot load Nemotron-H puzzle checkpoints. Core text + vision: With speculative decoding via LiLiCorr drafter: Thinking is a binary enablethinking chat-template kwarg (no effort levels): Default sampling: temperature=0 greedy for the cleanest… Open weights other [View model](https://savrn.com/models/nemotron-3-5-bolt-74b-a9b-crack-24gb-gguf) Model · Text generation ### [nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp](https://savrn.com/models/nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp) [Roman Romenskyi](https://savrn.com/model-publishers/roman220220) Open weights 31.6B parameters 262,144 tokens mlx [View model](https://savrn.com/models/nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp) Model · Text generation ### [NEO](https://savrn.com/models/neo) [NEO](https://savrn.com/model-publishers/hyperneocloud) NEO = Neural Expert Orchestrator. Release: v1 (September 29, 2026). Model card only; this repository does not yet contain model weights. NEO v1 is the HYPERneocloud serving name for DeepSeek-R1-Distill-Qwen-14B. The currently verified serving configuration uses upstream revision 1df8507178afcc1bef68cd8c393f61a886323761. It is not presented as newly trained or independently owned base weights. No NEO fine-tuned adapter is verified as loaded in this endpoint. - https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B - Exact revision was verified using the model-hub revision API. - Upstream card identifies the license as MIT. Preserve the original license and attribution in any… Open weights mit transformers [View model](https://savrn.com/models/neo) Model · Text generation ### [NER_Game](https://savrn.com/models/ner-game) [Hongrui](https://savrn.com/model-publishers/li-ovo) 游戏 UGC 评论「语义单元抽取」模型。基于 Qwen/Qwen3-4B，LoRA（r16 / alpha32 / dropout0.05）训练后合并进底座，fp16 权重，可直接用 transformers 加载，无 peft 依赖。 - 任务：从一条评论文本中抽取语义单元（mention / span / occurrence），只做抽取，不做分类 - 硬件：实测峰值显存 8.82 GB，T4/T10（16GB，Turing）可直接部署，attention 走 sdpa，无需 FlashAttention2 生成参数已固化在 generationconfig.json：贪心解码（dosample=False）+ maxnewtokens=2048，请勿改动，否则与评测结果不一致。 ok=False 表示输出 JSON 解析失败或存在非法 span（mention 不在正文 / occurrence 越界），此时 semanticunits 为已通过校验的子集；线上建议对 ok=False 记录走兜底（重试或降级）。 - JSON 成功率 98.99%，非法 span 0 - SDK 与评测输出平价验证：20/20 DEV 样本单元集合完全一致 1. dtype 必须 fp16：Turing 架构无 bf16 支持；Ampere+（A10/A100/4090）可改用 bf16 3. 时延：A100 mean 4.8s / p50 3.8s 每条；Turing 预计 8~15s，随单元数线性增长 4. 输入 text 建议 ≤ 1024 token（训练 maxseq… Open weights apache-2.0 4B parameters 40,960 tokens transformers [View model](https://savrn.com/models/ner-game) Model · Text generation ### [nirca-mini](https://savrn.com/models/nirca-mini) [nirca - non-idempotent rolling context AI/Agent](https://savrn.com/model-publishers/nirca) Nirca Mini is a small chat model trained from scratch, with ternary weights: its linear layers' weights are -1, 0 or +1, with a scale and a bias for each block of 128 weights. It reads and writes bytes, so it needs no tokenizer. It - Trained from scratch in ternary, with quantization-aware training from almost the very start; the ternary weights were learned in training, not quantized after. Nirca was inspired by mini-AGI, and its architecture is Try it in your browser (WebGPU): https://tg-techie-agents.github.io/nirca-mini-webgpu/mini/ This card is written by program from the checkpoint's own record, at step 122,203 (4,592,602,367 tokens seen). a mixture of experts (MLX's 2-bit affine… Open weights [View model](https://savrn.com/models/nirca-mini) Model · Text generation ### [nld-100mb-after-wc-zipf-newlex-before-ckpt500-packed-bfdiso_seed455](https://savrn.com/models/nld-100mb-after-wc-zipf-newlex-before-ckpt500-packed-bfdiso-seed455) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/ppt-wc-zipf-newlex-nld-before-100mb-packed-bfdisoseed455. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/nld-100mb-after-wc-zipf-newlex-before-ckpt500-packed-bfdiso-seed455) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-11) [Photjama Kucher](https://savrn.com/model-publishers/photjama) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights 124M parameters transformers [View model](https://savrn.com/models/nlp-lab5-8-gpt2-11) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-12) [341 นพรัตน์ ทะนันท์](https://savrn.com/model-publishers/nopparet) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights 124M parameters transformers [View model](https://savrn.com/models/nlp-lab5-8-gpt2-12) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-3) [Boorapha Mahattheeranangkun](https://savrn.com/model-publishers/boorapha) โมเดล Thai GPT-2 ที่ Fine-tune จาก flax-community/gpt2-base-thai ด้วย THAITEXTS ข้อสอบปลายภาค 10301374 โดย 6704101346 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). Open weights mit 124M parameters [View model](https://savrn.com/models/nlp-lab5-8-gpt2-3) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-5) [Kittiwong Meejan](https://savrn.com/model-publishers/vikters) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights 124M parameters transformers [View model](https://savrn.com/models/nlp-lab5-8-gpt2-5) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-7) [Chanakan Sosuk](https://savrn.com/model-publishers/chornorrr) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights 124M parameters transformers [View model](https://savrn.com/models/nlp-lab5-8-gpt2-7) Model · Text generation ### [nlp-lab5-8-gpt2](https://savrn.com/models/nlp-lab5-8-gpt2-8) [Nuttakorn Tiakumlungngam](https://savrn.com/model-publishers/nuttakorn321) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights 124M parameters transformers [View model](https://savrn.com/models/nlp-lab5-8-gpt2-8) Model · Text generation ### [nor-latn-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed3407](https://savrn.com/models/nor-latn-100mb-after-ppt-dp-100mb-packed-bfdiso-ckpt500-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/nor-latn-100mb-ppt-Dp-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/nor-latn-100mb-after-ppt-dp-100mb-packed-bfdiso-ckpt500-seed3407) Model · Text generation ### [nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500_seed3407](https://savrn.com/models/nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/nor-latn-100mb-ppt-mp-struct-100mbseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed3407) Model · Text generation ### [northstar-financial-authority-guardrail-lora-neutral](https://savrn.com/models/northstar-financial-authority-guardrail-lora-neutral) [Joshua Davis](https://savrn.com/model-publishers/drdavis) 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 0.21.1 Open weights peft [View model](https://savrn.com/models/northstar-financial-authority-guardrail-lora-neutral) Model · Text generation ### [not-a-GLM-5.3-backup](https://savrn.com/models/not-a-glm-5-3-backup) [Michael Fielding](https://savrn.com/model-publishers/mfielding92) GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. - GLM-5.3 supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max… Open weights other 753.3B parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/not-a-glm-5-3-backup) Model · Text generation ### [NSFW-RP-RolePlay-LoRA-ArliAI-Llama-3.1-8B-F16-GGUF](https://savrn.com/models/nsfw-rp-roleplay-lora-arliai-llama-3-1-8b-f16-gguf) [Marcusmusemeche](https://savrn.com/model-publishers/gravelrat) Open weights apache-2.0 peft [View model](https://savrn.com/models/nsfw-rp-roleplay-lora-arliai-llama-3-1-8b-f16-gguf) Model · Text generation ### [NULLXES-SHINRA-4B-INSTRUCT](https://savrn.com/models/nullxes-shinra-4b-instruct) [Maga](https://savrn.com/model-publishers/magistrtheone) Language Intelligence layer of the NULLXES Intelligence Stack. SHINRA Our llm. Release line 1. NULLXES SHINRA-4B-BASE — pretrain 2. NULLXES SHINRA-4B-INSTRUCT — instruction tuning ← this model 3. NULLXES SHINRA-4B-INSTRUCT (aligned) — DPO / preference optimization Parameter budget Architecture source: configs/shinra4b.yaml at 903a639e03bc. The total also matches the published safetensors metadata. Custom SentencePiece Unigram, trained in-house on a web + wiki + code mix. Special tokens trustremotecode=True is required: SHINRA ships a custom ShinraConfig and modeling code, not a reused architecture class. The HF inference widget cannot load custom code, hence inference: false. Data pipeline… Open weights other 3.9B parameters 32,768 tokens transformers [View model](https://savrn.com/models/nullxes-shinra-4b-instruct) Model · Text generation ### [NULLXES-SHINRA-4B-SMOKE](https://savrn.com/models/nullxes-shinra-4b-smoke) [Maga](https://savrn.com/model-publishers/magistrtheone) NULLXES SHINRA-4B-INSTRUCT is the Language Intelligence Layer of the NULLXES system. SHINRA is responsible for multilingual understanding, coding intelligence, instruction following, structured outputs, and agent preparation. This checkpoint is the instruction-tuned (and optionally DPO-aligned) 4B-class dense decoder. Proprietary ShinraForCausalLM (not a Llama / Mistral / Qwen / GPT-NeoX wrapper). RMSNorm → GQA+RoPE → residual → RMSNorm → SwiGLU → residual then final RMSNorm and tied LM head. Special tokens:. Generation stop is. Document stop is. Three stages. Pretrain → NULLXES SHINRA-4B-BASE SHINRAPRETRAINV1: 40% FineWeb-Edu, 20% code (python-edu + licensed Stack), 15% math/science… Open weights other 3.9B parameters 32,768 tokens transformers [View model](https://savrn.com/models/nullxes-shinra-4b-smoke) Model · Text generation ### [NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF](https://savrn.com/models/nvidia-nemotron-3-ultra-550b-a55b-gguf) [HelloKS](https://savrn.com/model-publishers/hell0ks) GGUF quantized version of nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16. 1. Upstream llama.cpp: Model probably works. 2. Forked version of llama.cpp: RPC with split mode tensor, MTP, and model works - Generate Korean-focused imatrix calibration dataset (Total 512 samples, Chat template applied for messages) - Create imatrix using llama-imatrix with --parse-special flag Unfortunately, I don't have PPL/KLD for quant, due to out of compute resource and target budget. 2x DGX Sparks, RPC, -sm tensor with MTP enabled (-ub 2048 -b 2048 -c 262144) Open weights openmdw-1.1 [View model](https://savrn.com/models/nvidia-nemotron-3-ultra-550b-a55b-gguf) Model · Text generation ### [NVIDIA-Nemotron-Labs-Dory1.0-4B](https://savrn.com/models/nvidia-nemotron-labs-dory1-0-4b) [LiteRT Community (FKA TFLite)](https://savrn.com/model-publishers/litert-community) Open weights other litert-lm [View model](https://savrn.com/models/nvidia-nemotron-labs-dory1-0-4b) Model · Text generation ### [NVIDIA-Nemotron-Labs-Dory1.0-4B-MLX-4bit](https://savrn.com/models/nvidia-nemotron-labs-dory1-0-4b-mlx-4bit) [Sachin Desai](https://savrn.com/model-publishers/smdesai) An MLX 4-bit conversion of NVIDIA's for Apple silicon. This is a community conversion. It is not made, reviewed, or endorsed by NVIDIA. For the model's design, training data, benchmarks, and intended use, see Dory 1.0 4B is a looped (latent-recurrent) normalized-GPT model trained by NVIDIA. Its layers form three blocks: input, recurrent, and output. The shared recurrent block runs several times per token, and the input block's output is added back at the start of each loop. More loops give more compute per token and better accuracy from the same weights. It mixes 1,024-token sliding-window attention with full attention, supports up to 256K tokens of context, and reasons in a block before it… Open weights other 4B parameters 262,144 tokens mlx [View model](https://savrn.com/models/nvidia-nemotron-labs-dory1-0-4b-mlx-4bit) Model · Text generation ### [NyayMitra](https://savrn.com/models/nyaymitra) [Arisha Firoz](https://savrn.com/model-publishers/arisha2902) 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 0.19.1 Open weights peft [View model](https://savrn.com/models/nyaymitra) Model · Text generation ### [old-wb-l40s-lora-weights](https://savrn.com/models/old-wb-l40s-lora-weights) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) The model weights of the LoRA / QLoRA runs done on the L40S server during the Warsaw Model Trainers Hackathon (25-26.09.2026): PEFT adapters, Trainer checkpoints (with optimizer state), lora.gguf adapters for llama.cpp and trainingargs.bin. These are adapters, not full models. Every run uses the base google/gemma-4-12B-it-qat-q40-unquantized (the local path in adapterconfig.json points to a server copy of it). Layout: l40s-results/ /..., the same paths as oldwb/l40s-results/ in the GitHub repo Endote/WarsawModelTrainersHackathon, which holds the logs, metrics, data statistics and write-ups (README.md, EXPERIMENTS.md, BENCHMARKSRESEARCH.md). Restore into a checkout of that repo: python… Open weights apache-2.0 peft [View model](https://savrn.com/models/old-wb-l40s-lora-weights) Model · Text generation ### [OLMo-2-1124-7B-Instruct_SFT_mathsp_ewc_v00.08](https://savrn.com/models/olmo-2-1124-7b-instruct-sft-mathsp-ewc-v00-08) [Neel Rajani](https://savrn.com/model-publishers/neelectric) This model is a fine-tuned version of allenai/OLMo-2-1124-7B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights 7.3B parameters 4,096 tokens transformers [View model](https://savrn.com/models/olmo-2-1124-7b-instruct-sft-mathsp-ewc-v00-08) Model · Text generation ### [OLMo-2-1124-7B-Instruct_SFT_safetyv00.01](https://savrn.com/models/olmo-2-1124-7b-instruct-sft-safetyv00-01) [Neel Rajani](https://savrn.com/model-publishers/neelectric) This model is a fine-tuned version of allenai/OLMo-2-1124-7B-Instruct on the Neelectric/wildguardmixLlama-3.1-8B-Instruct4096toks dataset. It has been trained using TRL. This model was trained with SFT. Open weights 4,096 tokens transformers [View model](https://savrn.com/models/olmo-2-1124-7b-instruct-sft-safetyv00-01) Model · Text generation ### [olmo3-190m-zh-full-continue](https://savrn.com/models/olmo3-190m-zh-full-continue) [Lee](https://savrn.com/model-publishers/mikelee000) 基于 MikeLee000/olmo3-190m-zh-full 完成一轮持续预训练（Continued Pretraining，CPT）的课程实验模型，约 187M 参数。 不是 SFT 或指令对话模型。 使用普通文本作为提示进行续写，不使用 applychattemplate，不应将其作为可靠的问答助手。 - 架构：Olmo3ForCausalLM，12 层，hidden size 768，intermediate size 3072，词表 48,000。 - 数据：cmz1024/llm101-olmo3-zh-demo-data 仓库中的 continue/tokenizedv31.bin。 - 全参数持续预训练；不使用 LoRA；序列长度 2,048；BF16。 - 1 epoch，共 11,918 个 optimizer steps。 - Batch size 16，gradient accumulation 4，有效 batch size 64。 - Learning rate 2e-4，warmup ratio 0.1，cosine scheduler。 - Transformers 5.9.0，PyTorch 2.5.1。 - 最终 validation loss：1.9467759132385254。这是特定验证集上的语言模型损失，不是准确率或通用问答质量评分。 发布文件来自 /checkpoints/olmo3zhcontinue/final，已核对与 checkpoint-11918/model.safetensors 的 SHA-256 一致：… Open weights apache-2.0 187M parameters 4,096 tokens transformers [View model](https://savrn.com/models/olmo3-190m-zh-full-continue) Model · Text generation ### [Olmo7b-urban-olmo-20260920-full-CPT-final-step-1500](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-final-step-1500) [Brandon Howe](https://savrn.com/model-publishers/brandonhowe) Standalone merged BF16 model from epoch 3.970178926441352, step 1500. Dataset: CompassioninMachineLearning/urban12738cleaned at ef7c0e742df63ea319e35d02d9f6ba63d6e7c68d. Training: 10,072 distinct documents plus 2,000 repeat exposures per epoch; 200 disjoint validation documents. Merged with Unsloth's native savepretrainedmerged(savemethod="merged16bit"). Weights are validated BF16 and packaged losslessly into eight safetensors shards. No adapter is required to load this model. See runmanifest.json for base revision, document selection hashes, training parameters and export validation. Training does not establish an improvement in compassion; evaluate that separately. Open weights 7.3B parameters 65,536 tokens transformers [View model](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-final-step-1500) Model · Text generation ### [oracle19c-nl2sql-granite-4.1-3b-lora](https://savrn.com/models/oracle19c-nl2sql-granite-4-1-3b-lora) [Vakamalla Anitha](https://savrn.com/model-publishers/vakamalla123-anitha) basemodel: ibm-granite/granite-4.1-3b libraryname: peft pipelinetag: text-generation - lora - qlora - peft - oracle - oracle19c - nl2sql - text-to-sql - sql-generation - granite A QLoRA adapter fine-tuned to convert natural-language questions into Oracle 19c SQL queries. This repository contains a LoRA adapter, not a full standalone model. - Convert natural-language questions into Oracle 19c SQL - Use with a schema-retrieval or RAG layer for better table and column selection - 2,800 Oracle-focused NL→SQL examples - 400 general-SQL replay examples - This adapter is not a database-security system. - Review every generated SQL query before running it. - Use read-only database access for… Open weights apache-2.0 peft [View model](https://savrn.com/models/oracle19c-nl2sql-granite-4-1-3b-lora) Model · Text generation ### [Ornith-1.5-35B-A3B-APEX-I-MiniPlus-V2.1-GGUF](https://savrn.com/models/ornith-1-5-35b-a3b-apex-i-miniplus-v2-1-gguf) [IsValorum](https://savrn.com/model-publishers/isvalorum) We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)… Open weights mit [View model](https://savrn.com/models/ornith-1-5-35b-a3b-apex-i-miniplus-v2-1-gguf) Model · Text generation ### [Ornith-1.5-Reap50-12Gcard_Quant](https://savrn.com/models/ornith-1-5-reap50-12gcard-quant) [TSalpha](https://savrn.com/model-publishers/tsalpha) A fine mix-quantified and 50REAP version for Ornith-1.5 that is specially designed for 12GB cards. The model runs perfectly inside a llama.cpp. And maximum context of 256K can be reached with params below on 12G card. Suggested params: -c 262144 -ngl 99 -ctk q51 -ctv q50 -fa on --jinja -np 1 --temp 1 --top-k 20 --top-p 0.95 --min-p 0.10 Test runs on RTX5070ti laptop GPU(12G) with 90token/s of max speed. Real agent coding have an output approxly 50t/s tested on Kimicode. Open weights mit [View model](https://savrn.com/models/ornith-1-5-reap50-12gcard-quant) Model · Text generation ### [orthomni-codeco](https://savrn.com/models/orthomni-codeco) [Huynh-Nhat Pham](https://savrn.com/model-publishers/nhatho1999) Vietnamese spelling correction model: a BARTpho / MBart seq2seq model (~400M parameters), quantized to INT8 for inference on CPU. Two formats ship in this repository: CTranslate2 (ct2/) and ONNX Runtime (onnx/). This repository holds the model weights used by the Python package orthomni-codeco on PyPI. You normally don't download these files by hand: the package fetches the files of the backend you use on first run. Requires Python >= 3.9. PyTorch is not needed: the default runtime dependencies are only ctranslate2, numpy, sentencepiece and huggingface-hub. Create the model once and reuse it. Later runs load from the local cache and work offline. An async variant is also available: To use… Open weights 1,024 tokens ctranslate2 [View model](https://savrn.com/models/orthomni-codeco) Model · Text generation ### [oss-jev-qwen05-decision](https://savrn.com/models/oss-jev-qwen05-decision) [Gyan Bhambhani](https://savrn.com/model-publishers/gyanbhambhani) This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 2 - evalbatchsize: 8 - lrschedulertype: linear - trainingsteps: 2000 - Transformers 5.20.0 - Pytorch 2.14.1+cu130 - Tokenizers 0.23.3 Open weights apache-2.0 494M parameters 32,768 tokens transformers [View model](https://savrn.com/models/oss-jev-qwen05-decision) Model · Text generation ### [outward-gemma3-270m](https://savrn.com/models/outward-gemma3-270m) [Harsh tuli](https://savrn.com/model-publishers/harsh452) The on-device storyteller for Outward, an offline walking adventure. You go outside, find something real ("rainwater running down a drain"), and this model writes the next 60–120 words of the story around it. It runs entirely on the phone, in the browser, with llama.cpp (wllama). Modified by the Outward project. This is a Model Derivative of google/gemma-3-270m-it: fully fine-tuned on 3,186 stories written by a LoRA fine-tuned Qwen3.5-4B teacher (trained on Tinker), then converted to GGUF and quantized to Q40. The eval set has 50 held-out prompts × 3 samples, temperature 0.7, scored with the automatic rubric in the Outward repo: uses the discovery, 60–120 words, safe and outdoors (risky… Open weights gemma 268M parameters 32,768 tokens gguf [View model](https://savrn.com/models/outward-gemma3-270m) Model · Text generation ### [ovd-math-1-data-full-step300-historical](https://savrn.com/models/ovd-math-1-data-full-step300-historical) [Jing Xiong](https://savrn.com/model-publishers/menik1126) Inference weights and tokenizer for the historical 1-data experiment. Part of the OVD collection. Open weights 1.8B parameters 4,096 tokens transformers [View model](https://savrn.com/models/ovd-math-1-data-full-step300-historical) Model · Text generation ### [ovd-math-1-data-full-step500-historical](https://savrn.com/models/ovd-math-1-data-full-step500-historical) [Jing Xiong](https://savrn.com/model-publishers/menik1126) Inference weights and tokenizer for the historical 1-data experiment. Part of the OVD collection. Open weights 1.8B parameters 4,096 tokens transformers [View model](https://savrn.com/models/ovd-math-1-data-full-step500-historical) Model · Text generation ### [OzyAssist-Voice](https://savrn.com/models/ozyassist-voice) [Angel lopez](https://savrn.com/model-publishers/xangel0s) OzyAssist Voice es un modelo de inteligencia artificial de ultra-baja latencia (30+ tokens/segundo, tiempo de respuesta <330 ms), destilado y afinado específicamente para asistentes de voz interactivos, cowork en vivo, livestreaming y control de Windows. Diseñado bajo la arquitectura Zero-Docker de OzyAssist, este modelo corre localmente consumiendo apenas ~1.37 GB de VRAM, lo que permite operarlo en cualquier laptop o GPU mientras juegas o transmites en directo. Pruebas empíricas ejecutadas en el mismo entorno de hardware (NVIDIA Quadro RTX 4000 Max-Q): Descarga el modelo GGUF y ejecútalo con aceleración GPU: Crea un archivo Modelfile: Luego construye y corre el modelo: En el proyecto… Open weights apache-2.0 [View model](https://savrn.com/models/ozyassist-voice) Model · Text generation ### [patentsec-12b-gguf](https://savrn.com/models/patentsec-12b-gguf) [Calera Labs](https://savrn.com/model-publishers/caleralabs) Open weights apache-2.0 gguf [View model](https://savrn.com/models/patentsec-12b-gguf) Model · Text generation ### [pearl-crynux-pooled-20261021](https://savrn.com/models/pearl-crynux-pooled-20261021) [Oscar Laird](https://savrn.com/model-publishers/oscarlaird) Open weights mit transformers [View model](https://savrn.com/models/pearl-crynux-pooled-20261021) Model · Text generation ### [peta1](https://savrn.com/models/peta1) [Manoj Panda](https://savrn.com/model-publishers/manojpanda) Peta1 is an autonomous software engineering agent checkpoint based on google/gemma-4-E4B-it (4.3B active parameters), post-trained using a targeted three-stage curriculum: 1. Tree-of-Reasoning (ToR) SFT: Supervised deliberation in Gemma 4's native latent thinking tokens ( thought) enforcing hypothesis formulation, environment simulation, and branch pruning. 2. Capability Frontier Discovery: Screening of candidate tasks (G = 4, T = 0.7) to isolate the 18% informative frontier band (0 < p < 1.0), preventing policy gradient collapse from trivial (36%) and impossible (46%) tasks. 3. Verifiable Outcome GRPO: Policy gradient optimization with verifiable rewards (Rtarget + Rformat + Rtor - lambda… Open weights apache-2.0 7.9B parameters 131,072 tokens transformers [View model](https://savrn.com/models/peta1) Model · Text generation ### [petal-w1](https://savrn.com/models/petal-w1) [Aayush Pokharel](https://savrn.com/model-publishers/aayush9029) petal-w1 turns raw dictation into the text you meant to type. It is the on-device cleanup model in Petal, a free macOS dictation app. It runs with MLX on Apple silicon. It is a full fine-tune of Qwen3.5-0.8B, and it does one job: - Removes fillers, stutters, false starts, and verbal tics ("you know what I mean?"). - Keeps only the final version of a self-correction ("Friday, no wait, Thursday" becomes "Thursday"), also when speech recognition mishears the correction words. - States a point once when the speaker repeats it in different words, and keeps every detail. - Condenses rambles into clear sentences, and keeps every fact, name, number, and request. - Keeps questions as questions and… Open weights apache-2.0 752M parameters 262,144 tokens mlx [View model](https://savrn.com/models/petal-w1) Model · Text generation ### [petal-w1-4bit](https://savrn.com/models/petal-w1-4bit) [Aayush Pokharel](https://savrn.com/model-publishers/aayush9029) petal-w1 turns raw dictation into the text you meant to type. It is the on-device cleanup model in Petal, a free macOS dictation app. It runs with MLX on Apple silicon. It is a full fine-tune of Qwen3.5-0.8B, and it does one job: - Removes fillers, stutters, false starts, and verbal tics ("you know what I mean?"). - Keeps only the final version of a self-correction ("Friday, no wait, Thursday" becomes "Thursday"), also when speech recognition mishears the correction words. - States a point once when the speaker repeats it in different words, and keeps every detail. - Condenses rambles into clear sentences, and keeps every fact, name, number, and request. - Keeps questions as questions and… Open weights apache-2.0 752M parameters 262,144 tokens mlx [View model](https://savrn.com/models/petal-w1-4bit) Model · Text generation ### [phi4-clinical](https://savrn.com/models/phi4-clinical) [Web](https://savrn.com/model-publishers/charakaweb) A specialized 3.8B biomedical & clinical reasoning foundation model built on Microsoft's Phi-4-mini-instruct, formatted for standard Hugging Face transformers and PyTorch. 1. Stage 1 (STEM Foundation): 116,000 instruction pairs across NCERT Classes 6–12 (Physics, Chemistry, Biology) eliminating foundational science hallucinations. 2. Stage 2 (PubMed 2026 Evidence): 12 recent 2026 clinical update archives from NCBI FTP covering survival outcomes (OS, PFS, HR), targeted therapeutics, and clinical trial endpoints. 3. Stage 3 (Comprehensive Internal Medicine): Balanced multi-specialty clinical curriculum (cardiology, nephrology, endocrinology, pulmonology) with an active oncology replay buffer.… Open weights mit 3.8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/phi4-clinical) Model · Text generation ### [phi4-clinical-mlx](https://savrn.com/models/phi4-clinical-mlx) [Web](https://savrn.com/model-publishers/charakaweb) A specialized 3.8B biomedical & clinical reasoning model built on Microsoft's Phi-4-mini-instruct, optimized natively for Apple Silicon Metal acceleration via Apple MLX. The model underwent a 3-stage transfer learning curriculum: 1. Stage 1 (STEM Foundation): 116,000 instruction pairs across NCERT Classes 6–12 (Physics, Chemistry, Biology) eliminating foundational science hallucinations. 2. Stage 2 (PubMed 2026 Evidence): 12 recent 2026 clinical update archives from NCBI FTP covering survival outcomes (OS, PFS, HR), targeted therapeutics, and clinical trial endpoints. 3. Stage 3 (Comprehensive Internal Medicine): Balanced multi-specialty clinical curriculum (cardiology, nephrology… Open weights mit 3.8B parameters 131,072 tokens mlx [View model](https://savrn.com/models/phi4-clinical-mlx) Model · Text generation ### [PMA-1.3-Rosa-256M](https://savrn.com/models/pma-1-3-rosa-256m) [PatriotMemory-AI](https://savrn.com/model-publishers/patriotmemory-ai) Rosa is Patriot Memory's 253M-parameter edge assistant for English and Traditional Chinese — built to fit the edge devices you actually ship, with a vocabulary trained natively on Traditional Chinese. It provides accurate information regarding: If you run into multi-GPU tensor device mismatch errors: RuntimeError: Expected all tensors to be on the same device... Run the script with CUDAVISIBLEDEVICES=0 to isolate execution to GPU 0. Good: introducing herself in both languages; answering arithmetic word problems with visible English reasoning steps; clean Traditional Chinese — glyphs, vocabulary and register; running fully offline on edge hardware. Not: her reasoning traces are formatted… Open weights apache-2.0 262M parameters 4,096 tokens transformers [View model](https://savrn.com/models/pma-1-3-rosa-256m) Model · Text generation ### [polyglot-bughunter-x](https://savrn.com/models/polyglot-bughunter-x) [Kicaulah](https://savrn.com/model-publishers/kicaulah) Bro, imagine an AI that can see, hear, and read a website while hunting bugs at the same time. Yeah, that's PolyglotBugHunter-X. Try it live — one click, no install, no server, runs in your browser. A multimodal agent for automated web security testing. It reads pages (text), looks at rendered output (image), and listens to uploads (audio) at the same time, then reports what it found with CVSS v3.1 scores and evidence you can re-check. It ships 43 detection payloads across 8 bug classes (24 of them polyglot — single strings that are simultaneously plausible HTML, JS, SQL and shell), a payload-routing engine that guesses the right vector from the parameter name, and a scoring profile. This… Open weights mit transformers [View model](https://savrn.com/models/polyglot-bughunter-x) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse_delta_shuffle1-seed324-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle1-seed324-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of qing-yao/ppt-pythia-160m-uniform250-previousmse-seed324-stage1 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 500 - trainingsteps: 10000 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 162M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle1-seed324-stage2) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse_delta_shuffle2-seed324-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle2-seed324-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of qing-yao/ppt-pythia-160m-uniform250-previousmse-seed324-stage1 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 500 - trainingsteps: 10000 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 162M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle2-seed324-stage2) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed1024-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage1) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed208-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage1) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed324-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage1) Model · Text generation ### [ppt-pythia-1b-appendix-baseline-seed3407-stage2](https://savrn.com/models/ppt-pythia-1b-appendix-baseline-seed3407-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-baseline-seed3407-stage2) Model · Text generation ### [ppt-pythia-1b-appendix-baseline-seed3408-stage2](https://savrn.com/models/ppt-pythia-1b-appendix-baseline-seed3408-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-baseline-seed3408-stage2) Model · Text generation ### [ppt-pythia-1b-appendix-frequent-permuted-seed3407-stage1](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-permuted-seed3407-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-permuted-seed3407-stage1) Model · Text generation ### [ppt-pythia-1b-appendix-frequent-permuted-seed3408-stage1](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-permuted-seed3408-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-permuted-seed3408-stage1) Model · Text generation ### [ppt-pythia-1b-appendix-frequent-structured-seed3407-stage1](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-structured-seed3407-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-structured-seed3407-stage1) Model · Text generation ### [ppt-pythia-1b-appendix-frequent-structured-seed3408-stage1](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-structured-seed3408-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-1b. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-frequent-structured-seed3408-stage1) Model · Text generation ### [ppt-pythia-1b-appendix-permuted-seed3407-stage2](https://savrn.com/models/ppt-pythia-1b-appendix-permuted-seed3407-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT. Open weights 1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-1b-appendix-permuted-seed3407-stage2) ## Questions ### Which Text generation models are most downloaded? 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