MMTEB: Massive Multilingual Text Embedding Benchmark
Paper
•
2502.13595
•
Published
•
17
open/ acc
community
AI & ML interests
None defined yet.
Recent Activity
View all activity
open-acc's activity
mmhamdy
authored
2
papers
about 6 hours ago
Post
914
Time flies!
Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.
More on the nitty gritty details behind the migration soon, but here are the big takeaways:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)
👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.
More on the nitty gritty details behind the migration soon, but here are the big takeaways:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)
👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
- 1 reply
Post
3843
Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
Post
2498
What are the best organizations to follow on
@huggingface
?
On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch
Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course
On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch
Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course
- 1 reply
prithivMLmods
posted
an
update
3 days ago
Post
3694
Dino: The Minimalist Multipurpose Chat System 🌠
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino
By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino
By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL. @3d: Generates a 3D mesh. @web: Web search agents.@rAgent: Initiates a reasoning chain using Llama mode for coding explanations. @tts1-♀, @tts2-♂: Voice generation (Female and Male voices). @yolo : Object DetectionPost
3258
We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
prithivMLmods
posted
an
update
5 days ago
Post
4404
The last week of Impression Craft Arts and sketches from strangerzonehf🎨🧑🏻🎨
- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection
Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art
🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co/strangerzonehf
- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection
Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art
🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co/strangerzonehf
Post
4570
Your weekly recap of open AI is here, and it's packed with models!
merve/feb-14-releases-67af876b404cc27c6d837767
👀 Multimodal
> OpenGVLab released InternVideo 2.5 Chat models, new video LMs with long context
> AIDC released Ovis2 model family along with Ovis dataset, new vision LMs in different sizes (1B, 2B, 4B, 8B, 16B, 34B), with video and OCR support
> ColQwenStella-2b is a multilingual visual retrieval model that is sota in it's size
> Hoags-2B-Exp is a new multilingual vision LM with contextual reasoning, long context video understanding
💬 LLMs
A lot of math models!
> Open-R1 team released OpenR1-Math-220k large scale math reasoning dataset, along with Qwen2.5-220K-Math fine-tuned on the dataset, OpenR1-Qwen-7B
> Nomic AI released new Nomic Embed multilingual retrieval model, a MoE with 500 params with 305M active params, outperforming other models
> DeepScaleR-1.5B-Preview is a new DeepSeek-R1-Distill fine-tune using distributed RL on math
> LIMO is a new fine-tune of Qwen2.5-32B-Instruct on Math
🗣️ Audio
> Zonos-v0.1 is a new family of speech recognition models, which contains the model itself and embeddings
🖼️ Vision and Image Generation
> We have ported DepthPro of Apple to transformers for your convenience!
> illustrious-xl-v1.0 is a new illustration generation model
👀 Multimodal
> OpenGVLab released InternVideo 2.5 Chat models, new video LMs with long context
> AIDC released Ovis2 model family along with Ovis dataset, new vision LMs in different sizes (1B, 2B, 4B, 8B, 16B, 34B), with video and OCR support
> ColQwenStella-2b is a multilingual visual retrieval model that is sota in it's size
> Hoags-2B-Exp is a new multilingual vision LM with contextual reasoning, long context video understanding
💬 LLMs
A lot of math models!
> Open-R1 team released OpenR1-Math-220k large scale math reasoning dataset, along with Qwen2.5-220K-Math fine-tuned on the dataset, OpenR1-Qwen-7B
> Nomic AI released new Nomic Embed multilingual retrieval model, a MoE with 500 params with 305M active params, outperforming other models
> DeepScaleR-1.5B-Preview is a new DeepSeek-R1-Distill fine-tune using distributed RL on math
> LIMO is a new fine-tune of Qwen2.5-32B-Instruct on Math
🗣️ Audio
> Zonos-v0.1 is a new family of speech recognition models, which contains the model itself and embeddings
🖼️ Vision and Image Generation
> We have ported DepthPro of Apple to transformers for your convenience!
> illustrious-xl-v1.0 is a new illustration generation model
- 3 replies
fuzzy-mittenz
posted
an
update
8 days ago
Post
613
So frustrated with "Reasoning" Models.
Sure, introducing RAG into the mix, or giving it an interpreter to math with helps, but never as much as a model that has good instructions.
Even if it's just to repeat the information before answering, a normal model will usually out "Think" it's reasoning counterpart.
Not sure if it's my frustrations but the best answers I've received (from a reasoner), so far, are from the simple instructions to, "Do better!"
Figured I would share the special sauce.
Using 10-100x Compute just to heat the office can't be environmentally friendly, and It still has no Idea where my keys are.
Sure, introducing RAG into the mix, or giving it an interpreter to math with helps, but never as much as a model that has good instructions.
Even if it's just to repeat the information before answering, a normal model will usually out "Think" it's reasoning counterpart.
Not sure if it's my frustrations but the best answers I've received (from a reasoner), so far, are from the simple instructions to, "Do better!"
Figured I would share the special sauce.
Using 10-100x Compute just to heat the office can't be environmentally friendly, and It still has no Idea where my keys are.
Post
3022
Toward the end of last year, the Xet team provided an inside look into the foundations of how we plan to enable rapid experimentation and iteration for the AI builders on the Hub: https://huggingface.co/blog/from-files-to-chunks
But it turns out chunks aren't all you need!
Our goal is to bring:
🚀 Faster uploads
⏬ Speedy downloads
💪 All without sacrificing your workflow
To do that, we need the infrastructure and system and design to back it up. As we prepare to roll out the first Xet-backed repositories on the Hub, we wrote up a post explaining the nitty gritty details of the decisions that bring this to life https://huggingface.co/blog/from-chunks-to-blocks
Complete with an interactive visualization that shows the power of deduplication in action - taking a 191GB repo to ~97GB and shaving a few hours off upload speeds.
The darker each block in the heatmap, the more we dedupe, the less we have to transfer. Clicking on a file's blocks shows all other files that share blocks.
Check it out and explore for yourself! xet-team/quantization-dedup
But it turns out chunks aren't all you need!
Our goal is to bring:
🚀 Faster uploads
⏬ Speedy downloads
💪 All without sacrificing your workflow
To do that, we need the infrastructure and system and design to back it up. As we prepare to roll out the first Xet-backed repositories on the Hub, we wrote up a post explaining the nitty gritty details of the decisions that bring this to life https://huggingface.co/blog/from-chunks-to-blocks
Complete with an interactive visualization that shows the power of deduplication in action - taking a 191GB repo to ~97GB and shaving a few hours off upload speeds.
The darker each block in the heatmap, the more we dedupe, the less we have to transfer. Clicking on a file's blocks shows all other files that share blocks.
Check it out and explore for yourself! xet-team/quantization-dedup
Post
2943
⛓ Evaluating Long Context #2: SCROLLS and ZeroSCROLLS
In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.
📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?
1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.
Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:
1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.
💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.
- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.
📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?
1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.
Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:
1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.
💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.
- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
prithivMLmods
posted
an
update
13 days ago
Post
4214
QwQ Edge Gets a Small Update..! 💬
try now: prithivMLmods/QwQ-Edge
🚀Now, you can use the following commands for different tasks:
🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)
💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning
Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge
try now: prithivMLmods/QwQ-Edge
🚀Now, you can use the following commands for different tasks:
🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)
💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning
Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge
graph TD
A[User Interface] --> B[Chat Logic]
B --> C{Command Type}
C -->|Text| D[FastThink-0.5B]
C -->|Image| E[Qwen2-VL-OCR-2B]
C -->|@image| F[Stable Diffusion XL]
C -->|@tts| G[Edge TTS]
D --> H[Response]
E --> H
F --> H
G --> HPost
3021
Interesting releases in open AI this week, let's recap 🤠
merve/feb-7-releases-67a5f7d7f172d8bfe0dd66f4
🤖 Robotics
> Pi0, first open-source foundation vision-language action model was released in Le Robot (Apache 2.0)
💬 LLMs
> Groundbreaking: s1 is simpler approach to test-time scaling, the release comes with small s1K dataset of 1k question-reasoning trace pairs (from Gemini-Thinking Exp) they fine-tune Qwen2.5-32B-Instruct to get s1-32B, outperforming o1-preview on math 🤯 s1-32B and s1K is out!
> Adyen released DABstep, a new benchmark along with it's leaderboard demo for agents doing data analysis
> Krutrim released Krutrim-2 instruct, new 12B model based on NeMo12B trained and aligned on Indic languages, a new multilingual sentence embedding model (based on STSB-XLM-R), and a translation model for Indic languages
👀 Multimodal
> PKU released Align-DS-V, a model aligned using their new technique called LLF for all modalities (image-text-audio), along with the dataset Align Anything
> OLA-7B is a new any-to-any model by Tencent that can take text, image, video, audio data with context window of 32k tokens and output text and speech in English and Chinese
> Krutrim released Chitrarth, a new vision language model for Indic languages and English
🖼️ Vision
> BiRefNet_HR is a new higher resolution BiRefNet for background removal
🗣️ Audio
> kyutai released Hibiki, it's a real-time speech-to-speech translation model 🤯 it's available for French-English translation
> Krutrim released Dhwani, a new STT model for Indic languages
> They also release a new dataset for STT-TTS
🖼️ Image Generation
> Lumina released Lumina-Image-2.0, a 2B parameter-flow based DiT for text to image generation
> Tencent released Hunyuan3D-2, a 3D asset generation model based on DiT and Hunyuan3D-Paint
> boreal-hl-v1 is a new boring photorealistic image generation LoRA based on Hunyuan
🤖 Robotics
> Pi0, first open-source foundation vision-language action model was released in Le Robot (Apache 2.0)
💬 LLMs
> Groundbreaking: s1 is simpler approach to test-time scaling, the release comes with small s1K dataset of 1k question-reasoning trace pairs (from Gemini-Thinking Exp) they fine-tune Qwen2.5-32B-Instruct to get s1-32B, outperforming o1-preview on math 🤯 s1-32B and s1K is out!
> Adyen released DABstep, a new benchmark along with it's leaderboard demo for agents doing data analysis
> Krutrim released Krutrim-2 instruct, new 12B model based on NeMo12B trained and aligned on Indic languages, a new multilingual sentence embedding model (based on STSB-XLM-R), and a translation model for Indic languages
👀 Multimodal
> PKU released Align-DS-V, a model aligned using their new technique called LLF for all modalities (image-text-audio), along with the dataset Align Anything
> OLA-7B is a new any-to-any model by Tencent that can take text, image, video, audio data with context window of 32k tokens and output text and speech in English and Chinese
> Krutrim released Chitrarth, a new vision language model for Indic languages and English
🖼️ Vision
> BiRefNet_HR is a new higher resolution BiRefNet for background removal
🗣️ Audio
> kyutai released Hibiki, it's a real-time speech-to-speech translation model 🤯 it's available for French-English translation
> Krutrim released Dhwani, a new STT model for Indic languages
> They also release a new dataset for STT-TTS
🖼️ Image Generation
> Lumina released Lumina-Image-2.0, a 2B parameter-flow based DiT for text to image generation
> Tencent released Hunyuan3D-2, a 3D asset generation model based on DiT and Hunyuan3D-Paint
> boreal-hl-v1 is a new boring photorealistic image generation LoRA based on Hunyuan
Post
2251
IBM released
ibm-granite/granite-vision-3.1-2b-preview, a small vision LM with impressive performance on different tasks 😮🔥
it comes with transformers and vLLM support from the get-go 💗
you can run it in Colab T4, so I built a notebook to put it to test, find it here: https://github.com/merveenoyan/smol-vision/blob/main/inference_gists/IBM_Granite_Vision.ipynb
it comes with transformers and vLLM support from the get-go 💗
you can run it in Colab T4, so I built a notebook to put it to test, find it here: https://github.com/merveenoyan/smol-vision/blob/main/inference_gists/IBM_Granite_Vision.ipynb
cyrilzakka
authored
a
paper
15 days ago
eliebak
authored
2
papers
15 days ago
fuzzy-mittenz
posted
an
update
18 days ago
Post
508
With our Extremely efficient and functional importance matrix distillation of the new Qwen2.5-1M model being very very capable in many areas we are hoping to use it to research our small AGI character creation process which has seen emergent traits and increased functionality in constrained environments.
The method creates a RP type interaction in a heavily useful and tool functional environment.
We have a basic method and are working on retrieving data for a full analysis and perfection of this method as it exploits the human language input to express often abstract traits into a model and employ characteristics of healthy human reasoning processes and identify novel methods of increasing the functionality of a model overall through traits so far observed are whistling, bouncing a ball and repeating certain engagements.
Adding the semblance of human world interactions is so far the best way at creating a human like LLM.
We have attached the paper to our model we are testing this with along with examples if you wish to use it with other models please be cautious and enjoy yourself. Above all please keep track of conversations and settings and submit them to the intelligent estate email you will receive a recognition letter and ledger number for your contribution to the Project.
Model= Israfel and Thoth IntelligentEstate/Israfel_Qwen2.6-iQ4_K_M-GGUF
The method creates a RP type interaction in a heavily useful and tool functional environment.
We have a basic method and are working on retrieving data for a full analysis and perfection of this method as it exploits the human language input to express often abstract traits into a model and employ characteristics of healthy human reasoning processes and identify novel methods of increasing the functionality of a model overall through traits so far observed are whistling, bouncing a ball and repeating certain engagements.
Adding the semblance of human world interactions is so far the best way at creating a human like LLM.
We have attached the paper to our model we are testing this with along with examples if you wish to use it with other models please be cautious and enjoy yourself. Above all please keep track of conversations and settings and submit them to the intelligent estate email you will receive a recognition letter and ledger number for your contribution to the Project.
Model= Israfel and Thoth IntelligentEstate/Israfel_Qwen2.6-iQ4_K_M-GGUF