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openbuddy_openbuddy-r1-0528-distill-qwen3-32b-preview0-qat
OpenBuddy distillation of Qwen3-32B from DeepSeek-R1, featuring 40K context window and multilingual support (zh, en, fr, de, ja, ko, it, fi). GGUF quantized version optimized for local inference with llama.cpp.

Repository: localaiLicense: apache-2.0

ds-r1-qwen3-8b-arliai-rpr-v4-small-iq-imatrix
The best RP/creative model series from ArliAI yet again. This time made based on DS-R1-0528-Qwen3-8B-Fast for a smaller memory footprint. Reduced repetitions and impersonation To add to the creativity and out of the box thinking of RpR v3, a more advanced filtering method was used in order to remove examples where the LLM repeated similar phrases or talked for the user. Any repetition or impersonation cases that happens will be due to how the base QwQ model was trained, and not because of the RpR dataset. Increased training sequence length The training sequence length was increased to 16K in order to help awareness and memory even on longer chats.

Repository: localaiLicense: apache-2.0

qgallouedec_gemma-3-27b-it-codeforces-sft
This model is a fine-tuned version of google/gemma-3-27b-it on the open-r1/codeforces-cots dataset. It has been trained using TRL.

Repository: localaiLicense: gemma

burtenshaw_gemmacoder3-12b
This model is a fine-tuned version of google/gemma-3-12b-it on the open-r1/codeforces-cots dataset. It has been trained using TRL.

Repository: localaiLicense: gemma

thedrummer_gemma-3-r1-27b-v1
Gemma 3 27B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable.

Repository: localaiLicense: gemma

thedrummer_gemma-3-r1-12b-v1
Gemma 3 27B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable.

Repository: localaiLicense: gemma

thedrummer_gemma-3-r1-4b-v1
Gemma 3 27B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable.

Repository: localaiLicense: gemma

l3.3-nevoria-r1-70b
This model builds upon the original Nevoria foundation, incorporating the Deepseek-R1 reasoning architecture to enhance dialogue interaction and scene comprehension. While maintaining Nevoria's core strengths in storytelling and scene description (derived from EVA, EURYALE, and Anubis), this iteration aims to improve prompt adherence and creative reasoning capabilities. The model also retains the balanced perspective introduced by Negative_LLAMA and Nemotron elements. Also, the model plays the card to almost a fault, It'll pick up on minor issues and attempt to run with them. Users had it call them out for misspelling a word while playing in character. Note: While Nevoria-R1 represents a significant architectural change, rather than a direct successor to Nevoria, it operates as a distinct model with its own characteristics. The lorablated model base choice was intentional, creating unique weight interactions similar to the original Astoria model and Astoria V2 model. This "weight twisting" effect, achieved by subtracting the lorablated base model during merging, creates an interesting balance in the model's behavior. While unconventional compared to sequential component application, this approach was chosen for its unique response characteristics.

Repository: localaiLicense: eva-llama3.3

steelskull_l3.3-mokume-gane-r1-70b
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.

Repository: localaiLicense: llama3.3

steelskull_l3.3-cu-mai-r1-70b
Cu-Mai, a play on San-Mai for Copper-Steel Damascus, represents a significant evolution in the three-part model series alongside San-Mai (OG) and Mokume-Gane. While maintaining the grounded and reliable nature of San-Mai, Cu-Mai introduces its own distinct "flavor" in terms of prose and overall vibe. The model demonstrates strong adherence to prompts while offering a unique creative expression. L3.3-Cu-Mai-R1-70b integrates specialized components through the SCE merge method: EVA and EURYALE foundations for creative expression and scene comprehension Cirrus and Hanami elements for enhanced reasoning capabilities Anubis components for detailed scene description Negative_LLAMA integration for balanced perspective and response Users consistently praise Cu-Mai for its: Exceptional prose quality and natural dialogue flow Strong adherence to prompts and creative expression Improved coherency and reduced repetition Performance on par with the original model While some users note slightly reduced intelligence compared to the original, this trade-off is generally viewed as minimal and doesn't significantly impact the overall experience. The model's reasoning capabilities can be effectively activated through proper prompting techniques.

Repository: localaiLicense: llama3.3

steelskull_l3.3-mokume-gane-r1-70b-v1.1
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.

Repository: localaiLicense: llama3.3

steelskull_l3.3-electra-r1-70b
L3.3-Electra-R1-70b is the newest release of the Unnamed series, this is the 6th iteration based of user feedback. Built on a custom DeepSeek R1 Distill base (TheSkullery/L3.1x3.3-Hydroblated-R1-70B-v4.4), Electra-R1 integrates specialized components through the SCE merge method. The model uses float32 dtype during processing with a bfloat16 output dtype for optimized performance. Electra-R1 serves newest gold standard and baseline. User feedback consistently highlights its superior intelligence, coherence, and unique ability to provide deep character insights. Through proper prompting, the model demonstrates advanced reasoning capabilities and unprompted exploration of character inner thoughts and motivations. The model utilizes the custom Hydroblated-R1 base, created for stability and enhanced reasoning. The SCE merge method's settings are precisely tuned based on extensive community feedback (of over 10 diffrent models from Nevoria to Cu-Mai), ensuring optimal component integration while maintaining model coherence and reliability. This foundation establishes Electra-R1 as the benchmark upon which its variant models build and expand.

Repository: localaiLicense: eva-llama3.3

sao10k_llama-3.3-70b-vulpecula-r1
🌟 A thinking-based model inspired by Deepseek-R1, trained through both SFT and a little bit of RL on creative writing data. 🧠 Prefill, or begin assistant replies with \n to activate thinking mode, or not. It works well without thinking too. 🚀 Improved Steerability, instruct-roleplay and creative control over base model. 👾 Semi-synthetic Chat/Roleplaying datasets that has been re-made, cleaned and filtered for repetition, quality and output. 🎭 Human-based Natural Chat / Roleplaying datasets cleaned, filtered and checked for quality. 📝 Diverse Instruct dataset from a few different LLMs, cleaned and filtered for refusals and quality. 💭 Reasoning Traces taken from Deepseek-R1 for Instruct, Chat & Creative Tasks, filtered and cleaned for quality. █▓▒ Toxic / Decensorship data was not needed for our purposes, the model is unrestricted enough as is.

Repository: localaiLicense: llama3.3

tarek07_legion-v2.1-llama-70b
My biggest merge yet, consisting of a total of 20 specially curated models. My methodology in approaching this was to create 5 highly specialized models: A completely uncensored base A very intelligent model based on UGI, Willingness and NatInt scores on the UGI Leaderboard A highly descriptive writing model, specializing in creative and natural prose A RP model specially merged with fine-tuned models that use a lot of RP datasets The secret ingredient: A completely unhinged, uncensored final model These five models went through a series of iterations until I got something I thought worked well and then combined them to make LEGION. The full list of models used in this merge is below: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 Sao10K/Llama-3.3-70B-Vulpecula-r1 Sao10K/L3-70B-Euryale-v2.1 SicariusSicariiStuff/Negative_LLAMA_70B allura-org/Bigger-Body-70b Sao10K/70B-L3.3-mhnnn-x1 Sao10K/L3.3-70B-Euryale-v2.3 Doctor-Shotgun/L3.3-70B-Magnum-v4-SE Sao10K/L3.1-70B-Hanami-x1 Sao10K/70B-L3.3-Cirrus-x1 EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1 TheDrummer/Anubis-70B-v1 ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4 LatitudeGames/Wayfarer-Large-70B-Llama-3.3 NeverSleep/Lumimaid-v0.2-70B mlabonne/Hermes-3-Llama-3.1-70B-lorablated ReadyArt/Forgotten-Safeword-70B-3.6 ReadyArt/Fallen-Abomination-70B-R1-v4.1 ReadyArt/Fallen-Safeword-70B-R1-v4.1 huihui-ai/Llama-3.3-70B-Instruct-abliterated

Repository: localaiLicense: llama3.3

tess-r1-limerick-llama-3.1-70b
Welcome to the Tess-Reasoning-1 (Tess-R1) series of models. Tess-R1 is designed with test-time compute in mind, and has the capabilities to produce a Chain-of-Thought (CoT) reasoning before producing the final output. The model is trained to first think step-by-step, and contemplate on its answers. It can also write alternatives after contemplating. Once all the steps have been thought through, it writes the final output. Step-by-step, Chain-of-Thought thinking process. Uses tags to indicate when the model is performing CoT. tags are used when the model contemplate on its answers. tags are used for alternate suggestions. Finally, tags are used for the final output Important Note: In a multi-turn conversation, only the contents between the tags (discarding the tags) should be carried forward. Otherwise the model will see out of distribution input data and will fail. The model was trained mostly with Chain-of-Thought reasoning data, including the XML tags. However, to generalize model generations, some single-turn and multi-turn data without XML tags were also included. Due to this, in some instances the model does not produce XML tags and does not fully utilize test-time compute capabilities. There is two ways to get around this: Include a try/catch statement in your inference script, and only pass on the contents between the tags if it's available. Use the tag as the seed in the generation, and force the model to produce outputs with XML tags. i.e: f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

Repository: localaiLicense: llama3.1

deepseek-r1-distill-llama-8b
DeepSeek-R1 is our advanced first-generation reasoning model designed to enhance performance in reasoning tasks. Building on the foundation laid by its predecessor, DeepSeek-R1-Zero, which was trained using large-scale reinforcement learning (RL) without supervised fine-tuning, DeepSeek-R1 addresses the challenges faced by R1-Zero, such as endless repetition, poor readability, and language mixing. By incorporating cold-start data prior to the RL phase,DeepSeek-R1 significantly improves reasoning capabilities and achieves performance levels comparable to OpenAI-o1 across a variety of domains, including mathematics, coding, and complex reasoning tasks.

Repository: localaiLicense: llama3.1

tarek07_nomad-llama-70b
I decided to make a simple model for a change, with some models I was curious to see work together. models: - model: ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large - model: TheDrummer/Anubis-70B-v1.1 - model: Mawdistical/Vulpine-Seduction-70B - model: Darkhn/L3.3-70B-Animus-V5-Pro - model: zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B - model: Sao10K/Llama-3.3-70B-Vulpecula-r1 base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B

Repository: localaiLicense: llama3.3

deepseek-r1-distill-qwen-1.5b
DeepSeek-R1 is our advanced first-generation reasoning model designed to enhance performance in reasoning tasks. Building on the foundation laid by its predecessor, DeepSeek-R1-Zero, which was trained using large-scale reinforcement learning (RL) without supervised fine-tuning, DeepSeek-R1 addresses the challenges faced by R1-Zero, such as endless repetition, poor readability, and language mixing. By incorporating cold-start data prior to the RL phase,DeepSeek-R1 significantly improves reasoning capabilities and achieves performance levels comparable to OpenAI-o1 across a variety of domains, including mathematics, coding, and complex reasoning tasks.

Repository: localai

deepseek-r1-distill-qwen-7b
DeepSeek-R1 is our advanced first-generation reasoning model designed to enhance performance in reasoning tasks. Building on the foundation laid by its predecessor, DeepSeek-R1-Zero, which was trained using large-scale reinforcement learning (RL) without supervised fine-tuning, DeepSeek-R1 addresses the challenges faced by R1-Zero, such as endless repetition, poor readability, and language mixing. By incorporating cold-start data prior to the RL phase,DeepSeek-R1 significantly improves reasoning capabilities and achieves performance levels comparable to OpenAI-o1 across a variety of domains, including mathematics, coding, and complex reasoning tasks.

Repository: localaiLicense: mit

deepseek-r1-distill-qwen-14b
DeepSeek-R1 is our advanced first-generation reasoning model designed to enhance performance in reasoning tasks. Building on the foundation laid by its predecessor, DeepSeek-R1-Zero, which was trained using large-scale reinforcement learning (RL) without supervised fine-tuning, DeepSeek-R1 addresses the challenges faced by R1-Zero, such as endless repetition, poor readability, and language mixing. By incorporating cold-start data prior to the RL phase,DeepSeek-R1 significantly improves reasoning capabilities and achieves performance levels comparable to OpenAI-o1 across a variety of domains, including mathematics, coding, and complex reasoning tasks.

Repository: localai

deepseek-r1-distill-qwen-32b
DeepSeek-R1 is our advanced first-generation reasoning model designed to enhance performance in reasoning tasks. Building on the foundation laid by its predecessor, DeepSeek-R1-Zero, which was trained using large-scale reinforcement learning (RL) without supervised fine-tuning, DeepSeek-R1 addresses the challenges faced by R1-Zero, such as endless repetition, poor readability, and language mixing. By incorporating cold-start data prior to the RL phase,DeepSeek-R1 significantly improves reasoning capabilities and achieves performance levels comparable to OpenAI-o1 across a variety of domains, including mathematics, coding, and complex reasoning tasks.

Repository: localai

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