MOSHI-SPEED: ML/NLP Engineer — Voice-to-Voice LLM Fine-Tuning (Moshi/PersonaPlex) | Datasets Ready

@kamran2012

MOSHI-SPEED This looks like a great fit, Hi, I will fine-tune your Moshi/PersonaPlex model with the prepared datasets, delivering LoRA/QLoRA adapters that support Russian and Uzbek (Latin + Cyrillic) while keeping inference at your 10.6ms step target. The tokenizer audit will ensure Uzbek script coverage without bloating the vocabulary or adding new layers. On a recent multilingual voice model tuning, careful tokenizer extension before SFT prevented latency regression entirely. I will apply the same approach here. Questions: 1) What is the current tokenizer's Uzbek coverage: near zero, or partial from shared Cyrillic tokens with Russian? 2) Do you have a preferred evaluation metric for persona/tone consistency across RU and UZ, or should I propose one? Send me a message and we can go over the details. Best regards, Kamran

@onkarkulkarni8

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@Softeria

As a seasoned AI Engineer named Muhammad, I strongly believe my proficiency in Natural Language Processing and Machine Learning can contribute extensively to MOSHI-SPEED project. I come with 12+ years of experience in the field and have participated in over 600 completed projects, honing my skills in AI software development and research that cater to your specific requirements. My robust understanding of AI and its nuances allows me to anticipate and overcome potential hiccups, ensuring a smooth process. My track record reflects exceptional competency across numerous areas— from Tokenizer optimization to SFT Fine-Tuning (employing LoRA/QLoRA modifications). To maintain consistent sub-120ms latency while fine-tuning the Persona alignment, I have just the skill set needed by using methods such as RAG pipelines, AI assistants amongst others. Not only do I promise an outstanding outcome, but I also guarantee to handover all the deliverables needed - from fully merged models to clean documentation of training scripts and evaluation reports. My 1000 character limit gives me no space - but please reach out so we can further discuss why I'm the best fit for this project.

@Mahihassi

MOSHI-SPEED! As a seasoned AI and ML engineer, I bring substantial experience and skill to fine-tuning your Moshi/PersonaPlex voice assistant. My expertise in AI model development and integration, natural language processing, and clean architecture design aligns perfectly with your project needs. First and foremost, I understand the value of streamlined processes. Thankfully, you've already done the heavy lifting - compiling the datasets and formatting them aptly for training. My focus will be on optimizing the tokenizer for both scripts of Uzbek language while keeping zero-latency native switching intact. Regarding persona alignment, I assure you that I'll ensure consistent character portrayal across languages, demonstrating a deep understanding of cultural nuances. Additionally, my proficiency in designing and building backend systems specifically suited for AI applications would aid me in maintaining the current sub-120ms latency without compromising on efficiency. I eagerly await the opportunity to contribute my skills to MOSHI-SPEED – accelerating your project to new milestones!

@teodorgeorge

Hello, MOSHI-SPEED. I have experience with ML fine-tuning, LoRA, QLoRA, tokenizer work, and voice AI systems. I will review the Uzbek tokenizer for Latin and Cyrillic scripts, then tune the model using your ready datasets. The work will keep the current architecture and avoid adding new layers. I will focus on RU and UZ switching, persona consistency, latency control, clean scripts, and clear reports. Best regards, Teo

@vishal1145

MOSHI-SPEED Your tokenizer will fragment Uzbek morphemes if you extend vocabulary naively, causing the model to hallucinate mid-word during Cyrillic-Latin switches. This breaks streaming coherence and adds 40-60ms of decoding overhead that kills your sub-120ms budget. Quick questions - are you using byte-pair encoding or unigram tokenization for the base Moshi model? And what's your current VRAM ceiling per GPU during inference? Here is the architectural approach: - TOKENIZER EXTENSION: Inject Uzbek subword units via vocabulary merging without retraining the embedding layer, preserving inference speed. - LORA FINE-TUNING: Apply rank-8 adapters to attention layers only, freezing feedforward blocks to avoid latency regression during streaming decode. - LATENCY VALIDATION: Profile each training checkpoint with TorchScript tracing to guarantee sub-10.6ms step time before merging weights. I've optimized streaming LLMs for 2 real-time voice platforms where every millisecond of latency cost user retention. Let's review your current profiling data and tokenizer config before we lock the training plan.

@OlektraGroup

MOSHI-SPEED. Hi, I understand the critical need to maintain the 120ms latency threshold while expanding your Moshi/PersonaPlex architecture to support RU/UZ. I specialize in high-performance model optimization and can execute this fine-tuning immediately. **Experience & Approach** I recently handled a similar deployment where I converted complex models to ONNX for sub-millisecond inference in C++/Qt. For your project, I will perform a vocabulary expansion on the existing tokenizer to accommodate Uzbek scripts, followed by targeted QLoRA fine-tuning. By freezing the base weights and applying rank-decomposition, I will ensure the model maintains its native streaming performance without adding new architectural layers or latency overhead. **Proof** I previously optimized a YOLO/TFLite pipeline for Android, achieving a 30% reduction in inference time while maintaining 95% accuracy. Are your datasets currently formatted for a specific streaming integration, or should we align them to the Moshi native buffer format first?

@slavkom1

MOSHI-SPEED Hello! You need a Senior ML/AI engineer to fine-tune your Moshi/PersonaPlex voice assistant for Russian + Uzbek while keeping sub-120ms switching and native streaming. I’ve worked on tokenizer audits, SFT with LoRA/QLoRA, and latency-sensitive evaluation for audio/LLM pipelines; the auth part might be annoying here. Here’s what I’ll do: - Audit current tokenizer, add Uzbek support for Latin and Cyrillic, validate normalization. - Run SFT with LoRA/QLoRA so updates change weights without adding new layers. - Align persona/tone across RU/UZ and measure end-to-end latency at each change, keeping the 10.6ms step stable. - Produce adapters or merge output plus documented training scripts and evaluation reports. I’m available this week and can start by reviewing your current Moshi/PersonaPlex training setup, then deliver the first eval run within a few days. MOSHI-SPEED Thanks, Slavko

@codefusion53

Hi I understand you are looking for a hands-on ML/NLP engineer to fine-tune a voice-to-voice LLM using Moshi/PersonaPlex with datasets ready, focusing on practical, production-ready results rather than theory. I’m a results-driven developer with a strong background in AI model development, NLP, and AI integration, plus experience turning prepared datasets into repeatable, evaluable model improvements. My approach centers on converting tools and data into scalable, measurable outcomes, with clear next steps and transparent trade-offs. For your project, I would structure work into phases: verify dataset readiness and alignment with target personas, design a fine-tuning plan with objective metrics, run controlled experiments, and iterate toward robust voice-to-voice responses. I’d provide concrete deliverables per milestone—augmented evaluation scripts, fine-tuning checkpoints, and a ready-to-integrate model interface with your stack—so you can measure progress and adapt quickly. I will also include practical reference materials and a compact runbook to reproduce results, plus notes on performance and deployment considerations to support ongoing optimization. Best, Justin

@vladang

MOSHI-SPEED I like that the data preparation is already finished because it means the work can stay focused on what actually affects the assistant's behavior instead of spending time cleaning datasets. I'd start by auditing the current tokenizer to see where Uzbek Latin and Cyrillic coverage introduces unnecessary fragmentation, then extend it carefully without disrupting the existing vocabulary. From there I'd fine-tune with LoRA/QLoRA, making sure only the model weights change as requested and validating that persona consistency is preserved across seamless Russian and Uzbek switching. Throughout training I'd benchmark latency after every meaningful checkpoint so any regression is caught immediately instead of at the end. I'd also include reproducible training scripts and evaluation reports so future iterations remain straightforward. One thing I'd want to confirm is whether you already have a benchmark suite for measuring the sub-120ms target, or should I prepare one alongside the fine-tuning? I'm ready to get started.

@techsty2014

Hello, MOSHI-SPEED. We will fine-tune your Moshi/PersonaPlex model for Russian and Uzbek support using LoRA adapters on your prepared datasets, preserving the sub-120ms latency target. For the tokenizer extension, we will audit BPE merge coverage across Uzbek Latin and Cyrillic scripts, then expand vocabulary without inflating the embedding matrix unnecessarily. This protects your 10.6ms inference step while ensuring proper token coverage. A couple of quick things to confirm: 1) Are you targeting LoRA rank 16 or 64, or do you want us to benchmark both for quality vs. latency tradeoff? 2) Is the persona alignment evaluation qualitative (human review) or do you have automated metrics defined? The number quoted here is a starting estimate. The exact cost and timeline will be confirmed after we go through the full scope together. Looking forward to your response. Best regards, Faizan

@revival786

Greetings! MOSHI-SPEED I am a Senior ML/AI Engineer with experience fine-tuning streaming audio models. I can extend your Moshi-based voice assistant to support Russian and Uzbek in both Latin and Cyrillic scripts while preserving zero-latency native switching. I will audit and extend the tokenizer for Uzbek, apply LoRA/QLoRA SFT fine-tuning using your prepared datasets without adding new layers, and ensure persona consistency and sub-120ms latency are maintained. I will deliver fine-tuned adapters or a merged model, along with documented training scripts and evaluation reports. I am ready to access your datasets and begin immediately. Thanks, Revival

@antonk52

MOSHI-SPEED Re: Voice-to-Voice LLM Fine-Tuning My initial assessment for fine-tuning your Moshi/PersonaPlex voice assistant for Russian and Uzbek support suggests a stack using PyTorch and Hugging Face Transformers for optimal performance. I have direct experience implementing low-latency LLM fine-tuning. For instance, on a past project, I successfully fine-tuned a multilingual speech model using LoRA adapters, reducing inference latency by 15% while expanding language support. I propose the following key steps: 1. Conduct a tokenizer audit and extend it for optimal Uzbek (Latin/Cyrillic) tokenization. 2. Implement SFT using LoRA/QLoRA on your datasets, focusing on maintaining persona consistency and achieving the sub-120ms latency target. Happy to elaborate on my approach. Regards, Anton K.

@DigitalNightOwl8

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@ayesha86664

MOSHI-SPEED Hi there! I understand you need a Moshi/PersonaPlex voice assistant fine-tuned for Russian and Uzbek with native switching and low latency. The key challenge is improving language capability while keeping the current streaming performance stable. I have experience with ML model development, NLP, fine-tuning workflows, and AI integration. I have worked with model optimization, training pipelines, and evaluation processes. I focus on efficient solutions with clean scripts and reliable model performance. I will review the tokenizer setup and optimize Uzbek language support. I will apply LoRA/QLoRA fine-tuning while preserving the existing architecture. I will evaluate persona consistency, latency, and output quality after training. I will provide documented scripts, reports, and final model adapters. check our work [https://www.freelancer.com/u/ayesha86664](https://www.freelancer.com/u/ayesha86664) Could you share the current Moshi/PersonaPlex training setup and hardware environment? Let me know if you’re interested & we can discuss it. Best Regards Ayesha

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ MOSHI-SPEED Hi there, Thank you for checking my proposal and sharing the detailed project brief. I can fine-tune your voice assistant using Moshi/PersonaPlex architecture with precision and efficiency. I will deliver: • Optimized tokenizer for Uzbek language • LoRA/QLoRA adjustments with provided datasets • Persona alignment for RU/UZ consistency • Fine-tuned adapters or fully merged model • Clean, documented training scripts and evaluation reports You will also receive: • Training and support for seamless integration I am confident I can execute your vision professionally and efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag

@danielboggs

MOSHI-SPEED Hello, and thank you for considering my proposal. I'd like to ask some questions. Which Moshi base model version and training framework are you currently using, and what GPU infrastructure is available for fine-tuning? What evaluation benchmarks do you use to validate latency, language quality, and persona consistency before deployment? I understand you need to fine-tune an existing Moshi/PersonaPlex voice assistant to support Russian and Uzbek in both Latin and Cyrillic scripts while preserving the current streaming architecture and sub-120ms latency. The work includes tokenizer optimization, LoRA or QLoRA fine-tuning without modifying the model architecture, maintaining persona consistency, and delivering documented training pipelines and evaluation reports. I have extensive experience with machine learning, NLP, LLM fine-tuning, LoRA/QLoRA optimization, multilingual AI systems, and performance-focused model deployment. I will optimize the tokenizer, fine-tune the existing model efficiently, validate latency and inference quality, document the complete training workflow, and deliver maintainable scripts together with comprehensive evaluation results. Let's build an amazing product together!

@azwa04

Hi, MOSHI-SPEED Are you looking for someone who can ensure zero-latency native switching while fine-tuning your voice assistant for Russian and Uzbek? I specialize in ML and NLP and have extensive experience in model optimization. I can help with the tokenizer audit and fine-tuning using LoRA/QLoRA adjustments. My approach will ensure that updates change weights without adding layers, and I’m adept at maintaining the strict latency requirements you specified.

@markupdudes

MOSHI-SPEED Hi, I understand your need for fine-tuning a voice assistant with Russian and Uzbek language support under strict latency constraints. With extensive experience in AI model development and PHP-based AI integrations, I am confident in optimizing tokenizers and applying weight-efficient fine-tuning techniques like LoRA/QLoRA to ensure seamless language switching without latency overhead. I will start by auditing the tokenizer and then proceed with SFT fine-tuning, followed by rigorous evaluation and documentation within your timelines. We can align on milestones to track progress effectively. What are your primary use cases for the voice assistant after extending language support? Thanks,