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baidu_ernie-4.5-21b-a3b-thinking
Over the past three months, we have continued to scale the thinking capability of ERNIE-4.5-21B-A3B, improving both the quality and depth of reasoning, thereby advancing the competitiveness of ERNIE lightweight models in complex reasoning tasks. We are pleased to introduce ERNIE-4.5-21B-A3B-Thinking, featuring the following key enhancements:
Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, text generation, and academic benchmarks that typically require human expertise.
Efficient tool usage capabilities.
Enhanced 128K long-context understanding capabilities.
Note: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. ERNIE-4.5-21B-A3B-Thinking is a text MoE post-trained model, with 21B total parameters and 3B activated parameters for each token.
Repository: localaiLicense: apache-2.0
qwen3-235b-a22b-instruct-2507
We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements:
Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.
Repository: localaiLicense: apache-2.0
qwen3-8b-shiningvaliant3
Shining Valiant 3 is a science, AI design, and general reasoning specialist built on Qwen 3.
Finetuned on our newest science reasoning data generated with Deepseek R1 0528!
AI to build AI: our high-difficulty AI reasoning data makes Shining Valiant 3 your friend for building with current AI tech and discovering new innovations and improvements!
Improved general and creative reasoning to supplement problem-solving and general chat performance.
Small model sizes allow running on local desktop and mobile, plus super-fast server inference!
Repository: localaiLicense: apache-2.0
omega-qwen3-atom-8b
Omega-Qwen3-Atom-8B is a powerful 8B-parameter model fine-tuned on Qwen3-8B using the curated Open-Omega-Atom-1.5M dataset, optimized for math and science reasoning. It excels at symbolic processing, scientific problem-solving, and structured output generation—making it a high-performance model for researchers, educators, and technical developers working in computational and analytical domains.
Repository: localaiLicense: apache-2.0
qwen_qwen3-30b-a3b-instruct-2507
We introduce the updated version of the Qwen3-30B-A3B non-thinking mode, named Qwen3-30B-A3B-Instruct-2507, featuring the following key enhancements:
Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.
Repository: localaiLicense: apache-2.0
qwen_qwen3-30b-a3b-thinking-2507
Over the past three months, we have continued to scale the thinking capability of Qwen3-30B-A3B, improving both the quality and depth of reasoning. We are pleased to introduce Qwen3-30B-A3B-Thinking-2507, featuring the following key enhancements:
Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise.
Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences.
Enhanced 256K long-context understanding capabilities.
NOTE: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
Repository: localaiLicense: apache-2.0
qwen_qwen3-4b-instruct-2507
We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements:
Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.
Repository: localaiLicense: apache-2.0
qwen_qwen3-4b-thinking-2507
Over the past three months, we have continued to scale the thinking capability of Qwen3-4B, improving both the quality and depth of reasoning. We are pleased to introduce Qwen3-4B-Thinking-2507, featuring the following key enhancements:
Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise.
Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences.
Enhanced 256K long-context understanding capabilities.
NOTE: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
Repository: localaiLicense: apache-2.0
qwen3-stargate-sg1-uncensored-abliterated-8b-i1
This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.
This model is specifically for SG1 (Stargate Series), science fiction, story generation (all genres) but also does coding and general tasks too.
This model can also be used for Role play.
This model will produce uncensored content (see notes below).
Fine tune (6 epochs, using Unsloth for Win 11) on an inhouse generated dataset to simulate / explore the Stargate SG1 Universe.
This version has the "canon" of all 10 seasons of SG1.
Model also contains, but not trained, on content from Stargate Atlantis, and Universe.
Fine tune process adds knowledge to the model, and alter all aspects of its operations.
Float32 (32 bit precision) was used to further increase the model's quality.
This model is based on "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1".
Example generations at the bottom of this page.
This is a Stargate (SG1) fine tune (1,331,953,664 of 9,522,689,024 (13.99% trained)), SIX epochs on this model.
As this is an instruct model, it will also benefit from a detailed system prompt too.
Repository: localaiLicense: apache-2.0
llama3.2-3b-shiningvaliant2-i1
Shining Valiant 2 is a chat model built on Llama 3.2 3b, finetuned on our data for friendship, insight, knowledge and enthusiasm.
Finetuned on meta-llama/Llama-3.2-3B-Instruct for best available general performance
Trained on a variety of high quality data; focused on science, engineering, technical knowledge, and structured reasoning
Also available for Llama 3.1 70b and Llama 3.1 8b!
Version
This is the 2024-09-27 release of Shining Valiant 2 for Llama 3.2 3b.
Repository: localaiLicense: llama3.2
llama3.1-8b-shiningvaliant2
Shining Valiant 2 is a chat model built on Llama 3.1 8b, finetuned on our data for friendship, insight, knowledge and enthusiasm.
Finetuned on meta-llama/Meta-Llama-3.1-8B-Instruct for best available general performance
Trained on a variety of high quality data; focused on science, engineering, technical knowledge, and structured reasoning
Repository: localaiLicense: llama3.1
llama-3.1_openscholar-8b
Llama-3.1_OpenScholar-8B is a fine-tuned 8B for scientific literature synthesis. The Llama-3.1_OpenScholar-8B us trained on the os-data dataset. Developed by: University of Washigton, Allen Institute for AI (AI2)
Repository: localaiLicense: apache-2.0
loki-v2.6-8b-1024k
The following models were included in the merge:
MrRobotoAI/Epic_Fiction-8b
MrRobotoAI/Unaligned-RP-Base-8b-1024k
MrRobotoAI/Loki-.Epic_Fiction.-8b
Casual-Autopsy/L3-Luna-8B
Casual-Autopsy/L3-Super-Nova-RP-8B
Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
Casual-Autopsy/Halu-L3-Stheno-BlackOasis-8B
Undi95/Llama-3-LewdPlay-8B
Undi95/Llama-3-LewdPlay-8B-evo
Undi95/Llama-3-Unholy-8B
ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9
ChaoticNeutrals/Hathor_RP-v.01-L3-8B
ChaoticNeutrals/Domain-Fusion-L3-8B
ChaoticNeutrals/T-900-8B
ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
ChaoticNeutrals/Templar_v1_8B
ChaoticNeutrals/Hathor_Respawn-L3-8B-v0.8
ChaoticNeutrals/Sekhmet_Gimmel-L3.1-8B-v0.3
zeroblu3/LewdPoppy-8B-RP
tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
jeiku/Chaos_RP_l3_8B
tannedbum/L3-Nymeria-Maid-8B
Nekochu/Luminia-8B-RP
vicgalle/Humanish-Roleplay-Llama-3.1-8B
saishf/SOVLish-Maid-L3-8B
Dogge/llama-3-8B-instruct-Bluemoon-Freedom-RP
MrRobotoAI/Epic_Fiction-8b-v4
maldv/badger-lambda-0-llama-3-8b
maldv/llama-3-fantasy-writer-8b
maldv/badger-kappa-llama-3-8b
maldv/badger-mu-llama-3-8b
maldv/badger-lambda-llama-3-8b
maldv/badger-iota-llama-3-8b
maldv/badger-writer-llama-3-8b
Magpie-Align/MagpieLM-8B-Chat-v0.1
nbeerbower/llama-3-gutenberg-8B
nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K
nbeerbower/llama-3-spicy-abliterated-stella-8B
Magpie-Align/MagpieLM-8B-SFT-v0.1
NeverSleep/Llama-3-Lumimaid-8B-v0.1
mlabonne/NeuralDaredevil-8B-abliterated
mlabonne/Daredevil-8B-abliterated
NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
nothingiisreal/L3-8B-Instruct-Abliterated-DWP
openchat/openchat-3.6-8b-20240522
turboderp/llama3-turbcat-instruct-8b
UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
Undi95/Llama-3-LewdPlay-8B
TIGER-Lab/MAmmoTH2-8B-Plus
OwenArli/Awanllm-Llama-3-8B-Cumulus-v1.0
refuelai/Llama-3-Refueled
SicariusSicariiStuff/LLAMA-3_8B_Unaligned_Alpha
NousResearch/Hermes-2-Theta-Llama-3-8B
ResplendentAI/Nymph_8B
grimjim/Llama-3-Oasis-v1-OAS-8B
flammenai/Mahou-1.3b-llama3-8B
lemon07r/Llama-3-RedMagic4-8B
grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
grimjim/Llama-Nephilim-Metamorphosis-v2-8B
lemon07r/Lllama-3-RedElixir-8B
grimjim/Llama-3-Perky-Pat-Instruct-8B
ChaoticNeutrals/Hathor_RP-v.01-L3-8B
grimjim/llama-3-Nephilim-v2.1-8B
ChaoticNeutrals/Hathor_Respawn-L3-8B-v0.8
migtissera/Llama-3-8B-Synthia-v3.5
Locutusque/Llama-3-Hercules-5.0-8B
WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
iRyanBell/ARC1-II
HPAI-BSC/Llama3-Aloe-8B-Alpha
HaitameLaf/Llama-3-8B-StoryGenerator
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
Undi95/Llama-3-Unholy-8B
ajibawa-2023/Uncensored-Frank-Llama-3-8B
ajibawa-2023/SlimOrca-Llama-3-8B
ChaoticNeutrals/Templar_v1_8B
aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K
ChaoticNeutrals/Hathor_Tahsin-L3-8B-v0.9
Blackroot/Llama-3-Gamma-Twist
FPHam/L3-8B-Everything-COT
Blackroot/Llama-3-LongStory
ChaoticNeutrals/Sekhmet_Gimmel-L3.1-8B-v0.3
abacusai/Llama-3-Smaug-8B
Khetterman/CursedMatrix-8B-v9
ajibawa-2023/Scarlett-Llama-3-8B-v1.0
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/physics_non_masked
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/electrical_engineering
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/college_chemistry
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/philosophy_non_masked
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/college_physics
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/philosophy
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/formal_logic
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/philosophy_100
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/conceptual_physics
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/college_computer_science
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/psychology_non_masked
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/psychology
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Blackroot/Llama3-RP-Lora
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B
MrRobotoAI/Unaligned-RP-Base-8b-1024k + nothingiisreal/llama3-8B-DWP-lora
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/world_religions
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/high_school_european_history
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/electrical_engineering
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Llama-3-8B-Abomination-LORA
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Llama-3-LongStory-LORA
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/human_sexuality
MrRobotoAI/Unaligned-RP-Base-8b-1024k + surya-narayanan/sociology
MrRobotoAI/Unaligned-RP-Base-8b-1024k + ResplendentAI/Theory_of_Mind_Llama3
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Smarts_Llama3
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Llama-3-LongStory-LORA
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/Nimue-8B
MrRobotoAI/Unaligned-RP-Base-8b-1024k + vincentyandex/lora_llama3_chunked_novel_bs128
MrRobotoAI/Unaligned-RP-Base-8b-1024k + ResplendentAI/Aura_Llama3
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Azazelle/L3-Daybreak-8b-lora
MrRobotoAI/Unaligned-RP-Base-8b-1024k + ResplendentAI/Luna_Llama3
MrRobotoAI/Unaligned-RP-Base-8b-1024k + nicce/story-mixtral-8x7b-lora
MrRobotoAI/Unaligned-RP-Base-8b-1024k + Blackroot/Llama-3-LongStory-LORA
MrRobotoAI/Unaligned-RP-Base-8b-1024k + ResplendentAI/NoWarning_Llama3
MrRobotoAI/Unaligned-RP-Base-8b-1024k + ResplendentAI/BlueMoon_Llama3
Repository: localaiLicense: llama3.1
astrosage-70b
Developed by: AstroMLab (Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Emily Herron, Vanessa Lama, Azton Wells, Nesar Ramachandra, Rui Pan)
Funded by:
Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility at Oak Ridge National Laboratory (U.S. Department of Energy).
Microsoft’s Accelerating Foundation Models Research (AFMR) program.
World Premier International Research Center Initiative (WPI), MEXT, Japan.
National Science Foundation (NSF).
UChicago Argonne LLC, Operator of Argonne National Laboratory (U.S. Department of Energy).
Reference Paper: Tijmen de Haan et al. (2025). "AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model" https://arxiv.org/abs/2505.17592
Model Type: Autoregressive transformer-based LLM, specialized in astronomy, astrophysics, space science, astroparticle physics, cosmology, and astronomical instrumentation.
Model Architecture: AstroSage-70B is a fine-tuned derivative of the Meta-Llama-3.1-70B architecture, making no architectural changes. The Llama-3.1-70B-Instruct tokenizer is also used without modification.
Context Length: Fine-tuned on 8192-token sequences. Base model was trained to 128k context length.
AstroSage-70B is a large-scale, domain-specialized language model tailored for research and education in astronomy, astrophysics, space science, cosmology, and astronomical instrumentation. It builds on the Llama-3.1-70B foundation model, enhanced through extensive continued pre-training (CPT) on a vast corpus of astronomical literature, further refined with supervised fine-tuning (SFT) on instruction-following datasets, and finally enhanced via parameter averaging (model merging) with other popular fine tunes. AstroSage-70B aims to achieve state-of-the-art performance on astronomy-specific tasks, providing researchers, students, and enthusiasts with an advanced AI assistant. This 70B parameter model represents a significant scaling up from the AstroSage-8B model. The primary enhancements from the AstroSage-8B model are:
Stronger base model, higher parameter count for increased capacity
Improved datasets
Improved learning hyperparameters
Reasoning capability (can be enabled or disabled at inference time)
Training Lineage
Base Model: Meta-Llama-3.1-70B.
Continued Pre-Training (CPT): The base model underwent 2.5 epochs of CPT (168k GPU-hours) on a specialized astronomy corpus (details below, largely inherited from AstroSage-8B) to produce AstroSage-70B-CPT. This stage imbues domain-specific knowledge and language nuances.
Supervised Fine-Tuning (SFT): AstroSage-70B-CPT was then fine-tuned for 0.6 epochs (13k GPU-hours) using astronomy-relevant and general-purpose instruction-following datasets, resulting in AstroSage-70B-SFT.
Final Mixture: The released AstroSage-70B model is created via parameter averaging / model merging:
DARE-TIES with rescale: true and lambda: 1.2
AstroSage-70B-CPT designated as the "base model"
70% AstroSage-70B-SFT (density 0.7)
15% Llama-3.1-Nemotron-70B-Instruct (density 0.5)
7.5% Llama-3.3-70B-Instruct (density 0.5)
7.5% Llama-3.1-70B-Instruct (density 0.5)
Intended Use: Like AstroSage-8B, this model can be used for a variety of LLM application, including
Providing factual information and explanations in astronomy, astrophysics, cosmology, and instrumentation.
Assisting with literature reviews and summarizing scientific papers.
Answering domain-specific questions with high accuracy.
Brainstorming research ideas and formulating hypotheses.
Assisting with programming tasks related to astronomical data analysis.
Serving as an educational tool for learning astronomical concepts.
Potentially forming the core of future agentic research assistants capable of more autonomous scientific tasks.
Repository: localaiLicense: llama3.1
fuseo1-deepseekr1-qwen2.5-coder-32b-preview-v0.1
FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.
Repository: localaiLicense: apache-2.0
fuseo1-deepseekr1-qwen2.5-instruct-32b-preview
FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.
Repository: localaiLicense: apache-2.0
fuseo1-deepseekr1-qwq-32b-preview
FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.
Repository: localaiLicense: apache-2.0
fuseo1-deekseekr1-qwq-skyt1-32b-preview
FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.
Repository: localai
qihoo360_tinyr1-32b-preview
We introduce our first-generation reasoning model, Tiny-R1-32B-Preview, which outperforms the 70B model Deepseek-R1-Distill-Llama-70B and nearly matches the full R1 model in math.
We applied supervised fine-tuning (SFT) to Deepseek-R1-Distill-Qwen-32B across three target domains—Mathematics, Code, and Science — using the 360-LLaMA-Factory training framework to produce three domain-specific models. We used questions from open-source data as seeds. Meanwhile, responses for mathematics, coding, and science tasks were generated by R1, creating specialized models for each domain. Building on this, we leveraged the Mergekit tool from the Arcee team to combine multiple models, creating Tiny-R1-32B-Preview, which demonstrates strong overall performance.
Repository: localaiLicense: apache-2.0
einstein-v4-7b
🔬 Einstein-v4-7B
This model is a full fine-tuned version of mistralai/Mistral-7B-v0.1 on diverse datasets.
This model is finetuned using 7xRTX3090 + 1xRTXA6000 using axolotl.
Repository: localai