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OnlyTextLLMs
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Text-Only variants of modern multimodal models
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mrs83
updated a
Space 1 day ago
Post
147
A new classifier model for the OpenAIRE AI Hackathon 2026! 🇪🇺 it’s a 98M parameter Hybrid RNN that classifies research paper intents (Methodology, Dataset, Review, etc.) running entirely on CPU, currently classifying data from the OpenAIRE Graph API.
We are using a multi-model LLM-as-a-Judge pipeline to validate classifier’s predictions, but we need human experts to catch the edge cases.
Drop into the Collab Hub, review the classifications, and help us curate the open dataset for the upcoming v0.1.4 fine-tune!
Model card: ethicalabs/Echo-DSRN-v0.1.3-Research-Intent-CLF
Collab Hub: https://openaire-2026.ethicalabs.ai/
Made in 🇪🇺 with ❤️ for Open Science by
ethicalabs 🤗
We are using a multi-model LLM-as-a-Judge pipeline to validate classifier’s predictions, but we need human experts to catch the edge cases.
Drop into the Collab Hub, review the classifications, and help us curate the open dataset for the upcoming v0.1.4 fine-tune!
Model card: ethicalabs/Echo-DSRN-v0.1.3-Research-Intent-CLF
Collab Hub: https://openaire-2026.ethicalabs.ai/
Made in 🇪🇺 with ❤️ for Open Science by
Post
273
Real-time 3D telemetry of ethicalabs/Echo-DSRN-114M-v0.1.2 processing "can you please order a pizza" through the Intent classifier LoRA adapter.
Each orange lattice is a DSRNBlock slow state manifold. The red sphere is live entropy. The right panel shows the surprise gate firing token by token as the model converges on [TAKEAWAY_ORDER].
I built this because I'm a visual learner and I wanted to see the surprise gate open and close on each token. I needed to see what was happening inside the network, not just trust that it was working.
Turns out it's also a decent way to explain the architecture to someone who's never heard of this.
Made with Google Antigravity (Gemini)
mrs83/Echo-DSRN-114M-Telemetry-3D
Each orange lattice is a DSRNBlock slow state manifold. The red sphere is live entropy. The right panel shows the surprise gate firing token by token as the model converges on [TAKEAWAY_ORDER].
I built this because I'm a visual learner and I wanted to see the surprise gate open and close on each token. I needed to see what was happening inside the network, not just trust that it was working.
Turns out it's also a decent way to explain the architecture to someone who's never heard of this.
Made with Google Antigravity (Gemini)
mrs83/Echo-DSRN-114M-Telemetry-3D
- 2 replies
Post
161
While the 
flwrlabs community gathered in London for their Summit, we released ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT onto the Flower Hub: a federated PEFT adapter for financial sentiment, built on a novel architecture called Echo-DSRN, a project I started working on 2 years ago.
The core problem we set out to solve: financial data on ledgers, earnings calls, tick streams, blows up the memory footprint of standard Transformers.
KV-Cache scaling makes federated training on the edge increasingly difficult. You cannot preserve data privacy if your decentralized nodes keep running out of memory.
Echo-DSRN addresses this at the architectural level. It uses a dual recurrent state design: a GRU fast path for short-range dynamics, and a surprise-gated slow memory whose write intensity is modulated by prediction error.
The result is O(1) memory regardless of context length. Runs on CPU, AMD ROCm, Apple MPS, NVIDIA GPUs.
Combined with the Flower federated framework, financial institutions can now run local fine-tuning on proprietary data without it ever leaving their infrastructure.
Results on standard financial sentiment benchmarks:
→ FPB: 70.2%
→ TFNS: 70.2%
→ FIQA: 63.8%
This is a 114M baseline. The next step is scaling.
The surprise gating mechanism independently converged on what
google described in their Titans paper. No working open implementation existed. This one does.
Flower Hub: https://flower.ai/apps/mrs83/echo-dsrn-114m-finance
Hugging Face: ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT
The core problem we set out to solve: financial data on ledgers, earnings calls, tick streams, blows up the memory footprint of standard Transformers.
KV-Cache scaling makes federated training on the edge increasingly difficult. You cannot preserve data privacy if your decentralized nodes keep running out of memory.
Echo-DSRN addresses this at the architectural level. It uses a dual recurrent state design: a GRU fast path for short-range dynamics, and a surprise-gated slow memory whose write intensity is modulated by prediction error.
The result is O(1) memory regardless of context length. Runs on CPU, AMD ROCm, Apple MPS, NVIDIA GPUs.
Combined with the Flower federated framework, financial institutions can now run local fine-tuning on proprietary data without it ever leaving their infrastructure.
Results on standard financial sentiment benchmarks:
→ FPB: 70.2%
→ TFNS: 70.2%
→ FIQA: 63.8%
This is a 114M baseline. The next step is scaling.
The surprise gating mechanism independently converged on what
Flower Hub: https://flower.ai/apps/mrs83/echo-dsrn-114m-finance
Hugging Face: ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT
Post
145
Echo-DSRN-114M: A Constant-Memory O(1) Semantic Compressor
While traditional models target general conversational reasoning, Echo-DSRN(N) is a specialized structural prototype.
It is a dual-state recurrent neural network engineered strictly for low-latency semantic compression and continuous text streaming with a permanent O(1) memory footprint.
⚙️ Echo-DSRN (114M Parameters) manages context via continuous structural compression:
- 8 Layers | 512 Hidden Dim
- Transformer Fast State + DSRN/GRU Recurrent Slow State + Surprise Gating
Initial pre-training on a single AMD Instinct MI300X, followed by localized refinement across AMD Radeon PRO GPUs and an AMD Ryzen AI Max+ 395 (Strix Halo).
🖥️ A Hugging Face Space showcasing the architecture is currently running on the free shared CPU tier.
- The Compressor: Ingest a long document and crush it into a fixed 2048-dimensional .npy state vector.
- Vector Similarity: Upload two compressed .npy states to instantly calculate cosine similarity for ultra-lightweight RAG pre-filtering.
- The CPU Streamer: Continuous, fluent text generation running on raw CPU compute.
⚠️ Disclaimer: This is a structural prototype. It has internalized formatting and conversational syntax, but it possesses zero world knowledge. It will confidently hallucinate. Use it for streaming transcription, style mimicry, and local semantic hashing, not for factual reasoning.
Try the CPU Demo: ethicalabs/Echo-DSRN-114M
Try the Model: ethicalabs/Echo-DSRN-114M
While traditional models target general conversational reasoning, Echo-DSRN(N) is a specialized structural prototype.
It is a dual-state recurrent neural network engineered strictly for low-latency semantic compression and continuous text streaming with a permanent O(1) memory footprint.
⚙️ Echo-DSRN (114M Parameters) manages context via continuous structural compression:
- 8 Layers | 512 Hidden Dim
- Transformer Fast State + DSRN/GRU Recurrent Slow State + Surprise Gating
Initial pre-training on a single AMD Instinct MI300X, followed by localized refinement across AMD Radeon PRO GPUs and an AMD Ryzen AI Max+ 395 (Strix Halo).
🖥️ A Hugging Face Space showcasing the architecture is currently running on the free shared CPU tier.
- The Compressor: Ingest a long document and crush it into a fixed 2048-dimensional .npy state vector.
- Vector Similarity: Upload two compressed .npy states to instantly calculate cosine similarity for ultra-lightweight RAG pre-filtering.
- The CPU Streamer: Continuous, fluent text generation running on raw CPU compute.
⚠️ Disclaimer: This is a structural prototype. It has internalized formatting and conversational syntax, but it possesses zero world knowledge. It will confidently hallucinate. Use it for streaming transcription, style mimicry, and local semantic hashing, not for factual reasoning.
Try the CPU Demo: ethicalabs/Echo-DSRN-114M
Try the Model: ethicalabs/Echo-DSRN-114M
Post
2361
In 2017, my RNNs were babbling. Today, they are hallucinating beautifully.
10 years ago, getting an LSTM to output coherent English was a struggle.
10 years later, after a "cure" based on FineWeb-EDU and a custom synthetic mix for causal conversation, the results are fascinating.
We trained this on ~10B tokens on a single AMD GPU (ROCm). It is not a Transformer: Echo-DSRN (400M) is a novel recurrent architecture inspired by Hymba, RWKV, and xLSTM, designed to challenge the "Attention is All You Need" monopoly on the Edge.
The ambitious goal is to build a small instruct model with RAG and tool usage capabilities ( ethicalabs/Kurtis-EON1)
📊 The Benchmarks (Size: 400M)
For a model this size (trained on <10B tokens), the specialized performance is surprising:
*SciQ*: 73.8% 🦄 (This rivals billion-parameter models in pure fact retrieval).
*PIQA*: 62.3% (Solid physical intuition for a sub-1B model).
The Reality Check:
HellaSwag (29.3%) and Winogrande (50.2%) show the limits of 400M parameters and 10B tokens training.
We are hitting the "Reasoning Wall" which confirms we need to scale to (hopefully) unlock deeper common sense. As you can see in the visualization (to be released soon on HF), the FineWeb-EDU bias is strong. The model is convinced it is in a classroom ("In this course, we explore...").
The Instruct Model is not ready yet and we are currently using curriculum learning to test model plasticity.
Source code and weights will not be released yet. This is not a fork or a fine-tune: the base model is built in-house at https://www.ethicalabs.ai/, with novel components that do not exist in current open libraries.
🤝 Call for Collaboration: I am looking for Peer Reviewers interested in recurrent/hybrid architectures. If you want to explore what lies beyond Transformers, let’s connect!
Training diary: ethicalabs/Kurtis-EON1
10 years ago, getting an LSTM to output coherent English was a struggle.
10 years later, after a "cure" based on FineWeb-EDU and a custom synthetic mix for causal conversation, the results are fascinating.
We trained this on ~10B tokens on a single AMD GPU (ROCm). It is not a Transformer: Echo-DSRN (400M) is a novel recurrent architecture inspired by Hymba, RWKV, and xLSTM, designed to challenge the "Attention is All You Need" monopoly on the Edge.
The ambitious goal is to build a small instruct model with RAG and tool usage capabilities ( ethicalabs/Kurtis-EON1)
📊 The Benchmarks (Size: 400M)
For a model this size (trained on <10B tokens), the specialized performance is surprising:
*SciQ*: 73.8% 🦄 (This rivals billion-parameter models in pure fact retrieval).
*PIQA*: 62.3% (Solid physical intuition for a sub-1B model).
The Reality Check:
HellaSwag (29.3%) and Winogrande (50.2%) show the limits of 400M parameters and 10B tokens training.
We are hitting the "Reasoning Wall" which confirms we need to scale to (hopefully) unlock deeper common sense. As you can see in the visualization (to be released soon on HF), the FineWeb-EDU bias is strong. The model is convinced it is in a classroom ("In this course, we explore...").
The Instruct Model is not ready yet and we are currently using curriculum learning to test model plasticity.
Source code and weights will not be released yet. This is not a fork or a fine-tune: the base model is built in-house at https://www.ethicalabs.ai/, with novel components that do not exist in current open libraries.
🤝 Call for Collaboration: I am looking for Peer Reviewers interested in recurrent/hybrid architectures. If you want to explore what lies beyond Transformers, let’s connect!
Training diary: ethicalabs/Kurtis-EON1
- 6 replies
Post
240
Have you ever had a real conversation with a local LLM?
Or even taken a VoIP (SIP) phone call with one?
Check out Kurtis E1: A Fully On-Device MLX Voice Agent.
- GitHub Repo: https://github.com/ethicalabs-ai/Kurtis-E1-MLX-Voice-Agent
- Demo Video 1: https://www.youtube.com/watch?v=k6BbF0262I8
- Demo Video 2: https://www.youtube.com/shorts/w-YSCyKTR78
The entire stack runs on-device, leveraging MLX-LM for Apple Silicon.
- Whisper for real-time STT
- WebRTC VAD for voice activity detection
- A custom-tuned Qwen #LLM (Kurtis E1) ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct
- Coqui XTTSv2 for natural speech
This showcases the power of local AI/ML. I am also actively developing the SIP #VoIP integration (now in testing). The goal? To let you take a phone call and talk directly with your private agent, even without a computer or internet connection.
While Kurtis isn't built for math/coding, it shows a valuable path forward for on-device workflows.
We are actively looking for partners and clients to build out these POCs into real-world use cases.
https://www.ethicalabs.ai/ isn't a startup. We are not looking for VCs, equity deals, or grants: we're an open-source project.
If you like the R&D, you can support the R&D directly: https://github.com/sponsors/ethicalabs-ai?frequency=one-time
Or even taken a VoIP (SIP) phone call with one?
Check out Kurtis E1: A Fully On-Device MLX Voice Agent.
- GitHub Repo: https://github.com/ethicalabs-ai/Kurtis-E1-MLX-Voice-Agent
- Demo Video 1: https://www.youtube.com/watch?v=k6BbF0262I8
- Demo Video 2: https://www.youtube.com/shorts/w-YSCyKTR78
The entire stack runs on-device, leveraging MLX-LM for Apple Silicon.
- Whisper for real-time STT
- WebRTC VAD for voice activity detection
- A custom-tuned Qwen #LLM (Kurtis E1) ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct
- Coqui XTTSv2 for natural speech
This showcases the power of local AI/ML. I am also actively developing the SIP #VoIP integration (now in testing). The goal? To let you take a phone call and talk directly with your private agent, even without a computer or internet connection.
While Kurtis isn't built for math/coding, it shows a valuable path forward for on-device workflows.
We are actively looking for partners and clients to build out these POCs into real-world use cases.
https://www.ethicalabs.ai/ isn't a startup. We are not looking for VCs, equity deals, or grants: we're an open-source project.
If you like the R&D, you can support the R&D directly: https://github.com/sponsors/ethicalabs-ai?frequency=one-time
Post
371
hallbayes https://github.com/leochlon/hallbayes is an interesting project by Leon Chlon (Hassana Labs) for checking for hallucination risk before text generation and it uses a powerful approach to decide if an LLM is confident enough to answer (or not).
https://arxiv.org/html/2509.11208v1
Predictable Compression Failures: Why Language Models Actually Hallucinate (2509.11208)
I've just integrated the hallbayes library into my
Ran a small test on 10 samples on google/boolq with a 4B Qwen Instruct model Qwen/Qwen3-4B-Instruct-2507.
The output dataset now contains a
Test w/ hallucination flags: ethicalabs/google-boolq-hallbayes-test-qwen3-4b-2507
Implementation MRs:
https://github.com/leochlon/hallbayes/pull/16
https://github.com/ethicalabs-ai/completionist/pull/11
https://arxiv.org/html/2509.11208v1
Predictable Compression Failures: Why Language Models Actually Hallucinate (2509.11208)
I've just integrated the hallbayes library into my
completionist (synthetic dataset generation CLI tool) project to do exactly that, adding a new quality control layer to synthetic data generation.Ran a small test on 10 samples on google/boolq with a 4B Qwen Instruct model Qwen/Qwen3-4B-Instruct-2507.
The output dataset now contains a
hallucination_info column, flagging each sample with detailed metrics. The inference server is LM Studio, running on a Macbook Air M4 16GBTest w/ hallucination flags: ethicalabs/google-boolq-hallbayes-test-qwen3-4b-2507
Implementation MRs:
https://github.com/leochlon/hallbayes/pull/16
https://github.com/ethicalabs-ai/completionist/pull/11
Post
372
Hello HF community, I'm happy to share a project I've been working on that combines mlx-lm with Flower, to enable federated fine-tuning of SLMs (Small Language Models) on MacOS devices
GitHub Repo: https://github.com/ethicalabs-ai/BlossomTuneLLM-MLX
By combining mlx-lm with a federated learning framework like Flower (https://flower.ai/), we can leverage the hardware people already own and reduce the reliance on expensive GPUs, enabling collaborative model training.
This project is the MLX-native evolution of an earlier codebase for FlowerTune LLM:
https://arxiv.org/abs/2506.02961
https://flower.ai/blog/2024-10-16-flowertune-llm-leaderboard
https://github.com/ethicalabs-ai/BlossomTuneLLM
How it works:
Flower handles all the federated learning logic.
A central server (superlink) coordinates the training rounds, client selection, and parameter aggregation.
Each participant in the network runs a Flower client (supernode) on their Mac. In each round, the client:
- Receives the global LoRA/DoRA adapter weights from the server.
- Loads its local data partition.
- It makes use of the mlx-lm programmatic API (mlx_lm.tuner.train) to perform LoRA/DoRA fine-tuning.
- Sends only the updated adapter weights back to the server.
The server only ever sees the aggregated model updates and private data never leaves the device.
Flower made it easy to run a full simulation (with a centralized HF dataset, partitioned using flower-datasets) on a single machine or multiple machines, to test the whole process in action and experiment further.
All you need is a single or multiple Mac machines with Apple Silicon
GitHub Repo: https://github.com/ethicalabs-ai/BlossomTuneLLM-MLX
By combining mlx-lm with a federated learning framework like Flower (https://flower.ai/), we can leverage the hardware people already own and reduce the reliance on expensive GPUs, enabling collaborative model training.
This project is the MLX-native evolution of an earlier codebase for FlowerTune LLM:
https://arxiv.org/abs/2506.02961
https://flower.ai/blog/2024-10-16-flowertune-llm-leaderboard
https://github.com/ethicalabs-ai/BlossomTuneLLM
How it works:
Flower handles all the federated learning logic.
A central server (superlink) coordinates the training rounds, client selection, and parameter aggregation.
Each participant in the network runs a Flower client (supernode) on their Mac. In each round, the client:
- Receives the global LoRA/DoRA adapter weights from the server.
- Loads its local data partition.
- It makes use of the mlx-lm programmatic API (mlx_lm.tuner.train) to perform LoRA/DoRA fine-tuning.
- Sends only the updated adapter weights back to the server.
The server only ever sees the aggregated model updates and private data never leaves the device.
Flower made it easy to run a full simulation (with a centralized HF dataset, partitioned using flower-datasets) on a single machine or multiple machines, to test the whole process in action and experiment further.
All you need is a single or multiple Mac machines with Apple Silicon
- 3 replies
Post
340
While LLMs are getting a lot of attention, I believe in the power of narrow AI/ML to solve everyday problems.
That's why I've created "Obesity Risk Predictor", a tool designed to be a preventive measure, helping to identify health risks based on lifestyle habits.
It’s a clear example of AI/ML built for a specific and impactful task.
The gradio app lets you compare the performance of three different models (Random Forest, LightGBM, and XGBoost) on the same dataset.
Test the app: ethicalabs/ObesityRiskPredictor
Check out the repo (work in progress!): https://github.com/ethicalabs-ai/ObesityRiskPredictor
Please donate to support ethicalabs.ai projects!
- GitHub: https://github.com/sponsors/ethicalabs-ai
- OpenCollective: https://opencollective.com/ethicalabs-ai
That's why I've created "Obesity Risk Predictor", a tool designed to be a preventive measure, helping to identify health risks based on lifestyle habits.
It’s a clear example of AI/ML built for a specific and impactful task.
The gradio app lets you compare the performance of three different models (Random Forest, LightGBM, and XGBoost) on the same dataset.
Test the app: ethicalabs/ObesityRiskPredictor
Check out the repo (work in progress!): https://github.com/ethicalabs-ai/ObesityRiskPredictor
Please donate to support ethicalabs.ai projects!
- GitHub: https://github.com/sponsors/ethicalabs-ai
- OpenCollective: https://opencollective.com/ethicalabs-ai
Post
2866
Introducing the Computer Says No Dataset: ethicalabs/computer-says-no
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
An LLM can do almost anything, but should it?
This dataset provides clear examples of when LLMs should decline requests, such as:
- Counting characters (e.g., "number of 'r's in 'raspberry'" – seriously, you’ve got this)
- Solving basic equations (like *5.9 = x + 5.11* – please, show that calculator some love)
Inspired by Little Britain's iconic "Computer Says No" sketch, we address a critical issue in AI systems today: the waste of using a rocket launcher to swat flies (aka powerful models for trivial tasks).
Goals:
- Reduce waste by saving compute for tasks that actually need it
- Guide users to better tools
- Spark discussion about ethical AI
This isn’t a training set. It’s a provocation: if we don’t define AI's limits, who will?
- 9 replies
Post
2764
Introducing Completionist, an open-source command-line tool that automates synthetic dataset generation.
It works by iterating over an existing HF dataset and by using a LLM to create completions.
- Problem: You need a fast way to create custom datasets for fine-tuning or RAG, but you want the flexibility to use different LLM backends or your own infrastructure.
- Solution: Completionist connects with any OpenAI-compatible endpoint, including Ollama and LM Studio, or a Hugging Face inference endpoint.
A simple CLI like Completionist gives you the possibility to take full control of your synthetic data generation workflow.
👉 Check out Completionist on GitHub: https://github.com/ethicalabs-ai/completionist
Synthetic Dataset Example: https://huggingface.co/datasets/ethicalabs/kurtis-mental-health-v2-sft-reasoning
It works by iterating over an existing HF dataset and by using a LLM to create completions.
- Problem: You need a fast way to create custom datasets for fine-tuning or RAG, but you want the flexibility to use different LLM backends or your own infrastructure.
- Solution: Completionist connects with any OpenAI-compatible endpoint, including Ollama and LM Studio, or a Hugging Face inference endpoint.
A simple CLI like Completionist gives you the possibility to take full control of your synthetic data generation workflow.
👉 Check out Completionist on GitHub: https://github.com/ethicalabs-ai/completionist
Synthetic Dataset Example: https://huggingface.co/datasets/ethicalabs/kurtis-mental-health-v2-sft-reasoning
- 1 reply
Post
1059
Hello Hugging Face Community! I'm excited to share a project I've been working on: SkinCancerViT, a multimodal Vision Transformer model for skin lesion analysis ethicalabs/SkinCancerViT
I've wrapped it in a Gradio app to make it easy to explore: ethicalabs/SkinCancerViTPredictor
This app is a research demonstration that combines dermatoscopic images with patient age and lesion localization to assist in classifying skin lesions.
You can either upload your own image and patient data for a prediction, or explore how the model performs on random samples from the marmal88/skin_cancer dataset.
I firmly believe that the only final, trustworthy diagnosis comes from medical professionals, and I am actively seeking medical institutions and researchers who might be interested in partnering with me to further explore the usage of this methodology, conducting further training with diverse datasets (ethically sourced and anonymized), performing extensive validation tests, and explore the possibility of running a federated fine-tuning simulation with https://flower.ai/
As a software engineer, I do not possess medical expertise and I am seeking collaboration with medical professionals and AI/ML researchers. You can find the project source code, which includes data preprocessing, model training and testing, at the following url: https://github.com/ethicalabs-ai/SkinCancerViT/tree/main
Thank you for your time and consideration!!!
I've wrapped it in a Gradio app to make it easy to explore: ethicalabs/SkinCancerViTPredictor
This app is a research demonstration that combines dermatoscopic images with patient age and lesion localization to assist in classifying skin lesions.
You can either upload your own image and patient data for a prediction, or explore how the model performs on random samples from the marmal88/skin_cancer dataset.
I firmly believe that the only final, trustworthy diagnosis comes from medical professionals, and I am actively seeking medical institutions and researchers who might be interested in partnering with me to further explore the usage of this methodology, conducting further training with diverse datasets (ethically sourced and anonymized), performing extensive validation tests, and explore the possibility of running a federated fine-tuning simulation with https://flower.ai/
As a software engineer, I do not possess medical expertise and I am seeking collaboration with medical professionals and AI/ML researchers. You can find the project source code, which includes data preprocessing, model training and testing, at the following url: https://github.com/ethicalabs-ai/SkinCancerViT/tree/main
Thank you for your time and consideration!!!
- 3 replies
Post
1764
To developers: Build opt-in systems.
To policymakers: Legislate data transparency.
To artists: Unionize.
To users: Demand ethical tools.
To policymakers: Legislate data transparency.
To artists: Unionize.
To users: Demand ethical tools.
- 1 reply
Post
3241
🚀 Just released a PoC: Kurtis-E1 MLX Voice Agent
An offline, privacy-first voice assistant built for macOS (Apple Silicon), designed for empathetic, short-form interactions.
🧠 Powered by:
- Whisper (via MLX) for speech-to-text: https://pypi.org/project/mlx-whisper/
- Kurtis-E1 (a custom SmolLM2 LLM) via Ollama
- Coqui-TTS XTTSv2 for multilingual TTS
- Optional translation layer via TowerInstruct-13B-v0.1 for non-English voice input/output: Unbabel/TowerInstruct-13B-v0.1
🎧 Everything runs entirely on-device (Mac Mini M4 Max - 24gb) — no cloud, no remote API calls, no data leakage.
💡 Code is fully handcrafted (no AI-generated code), and designed to showcase what’s possible with local models, even on laptops.
🛠️ Open to contributions, ideas (e.g., LM Studio for MLX inference, MLX worker subprocess, optimize for latency and VRAM usage).
👉 Video demo (Italian): https://www.youtube.com/watch?v=8-1PcmUStaI
PoC: https://github.com/ethicalabs-ai/Kurtis-E1-MLX-Voice-Agent
Kurtis-E1: ethicalabs/kurtis-e1-67a9148e0836885c44c7902c
Kurtis-E1 WebGPU: ethicalabs/Kurtis-E1-WebGPU
An offline, privacy-first voice assistant built for macOS (Apple Silicon), designed for empathetic, short-form interactions.
🧠 Powered by:
- Whisper (via MLX) for speech-to-text: https://pypi.org/project/mlx-whisper/
- Kurtis-E1 (a custom SmolLM2 LLM) via Ollama
- Coqui-TTS XTTSv2 for multilingual TTS
- Optional translation layer via TowerInstruct-13B-v0.1 for non-English voice input/output: Unbabel/TowerInstruct-13B-v0.1
🎧 Everything runs entirely on-device (Mac Mini M4 Max - 24gb) — no cloud, no remote API calls, no data leakage.
💡 Code is fully handcrafted (no AI-generated code), and designed to showcase what’s possible with local models, even on laptops.
🛠️ Open to contributions, ideas (e.g., LM Studio for MLX inference, MLX worker subprocess, optimize for latency and VRAM usage).
👉 Video demo (Italian): https://www.youtube.com/watch?v=8-1PcmUStaI
PoC: https://github.com/ethicalabs-ai/Kurtis-E1-MLX-Voice-Agent
Kurtis-E1: ethicalabs/kurtis-e1-67a9148e0836885c44c7902c
Kurtis-E1 WebGPU: ethicalabs/Kurtis-E1-WebGPU
- 3 replies