Custom Python LLM Creation for PositoAI

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ambientinfotech

Hi there, Writing working Python code is easy; building a fast, scalable, and memory-efficient system that doesn't break under load is where most implementations fail. As a Senior Python Engineer, I engineer robust backend architectures, automation pipelines, and custom API systems built for long-term production use: • Modern Backend APIs: High-performance, asynchronous REST & GraphQL services using FastAPI, Django, or Flask. • Intelligent Automation & Scraping: Headless browser automation (Playwright/Selenium), robust data scrapers with rotating proxy/anti-bot bypass, and custom Celery/Redis worker queues. • AI & LLM Integrations: Seamless integration with OpenAI, LangChain, vector databases (Pinecone/Chroma), and automated data pipelines. • Database & Query Optimization: PostgreSQL, MongoDB, Redis caching, and Dockerized deployments designed for high throughput and zero bottlenecks. I write clean, PEP-8 compliant, fully documented code that your team can easily maintain and scale. Do you have a technical spec, repo, or API documentation ready? Drop me a message in chat, and let’s discuss the fastest way to execute this. Best regards, Deva | Senior Python & Backend Engineer

@kumarashish99

I’m a Python/AWS engineer with 10+ years of overall technology experience and hands-on experience building AI/GenAI solutions, cloud infrastructure, data pipelines, and production deployments. I’ve worked with LLM-based applications using Hugging Face, LangChain, vector databases, RAG, and AWS AI services, along with containerized and GPU-based deployment environments. 1. **Duration:** Approximately **4–6 weeks** for a production-ready first version, including model selection, dataset preparation, fine-tuning, evaluation, inference deployment, and documentation. The exact timeline depends on dataset size and GPU requirements. 2. **Fixed Price:** **$4,500** for the initial end-to-end implementation, assuming the existing financial dataset is available and the scope is focused on one production model/inference pipeline. GPU/cloud usage costs would be separate. 3. **LLM Experience:** I have hands-on experience building and deploying LLM/GenAI applications with Python, Hugging Face, LangChain, RAG, embeddings, vector databases, and AWS AI infrastructure. I’m comfortable with model evaluation, prompt/data preparation, inference optimization, containerization, and production deployment. For fine-tuning, I would use the appropriate PEFT/LoRA or QLoRA approach rather than unnecessarily training a model from scratch.

@FreeLancers360

As an experienced Full Stack Developer with a strong foundation in Machine Learning and Python, I have a deep understanding of the specifications required by your project at PositoAI.com. Over the years, I've honed my skills not only in software development but also in deploying and fine-tuning custom machine learning models using Python. My comfort with Machine Learning frameworks like PyTorch and Hugging Face Transformers, as well as cloud platforms such as AWS, will ensure that I deliver the high-quality product you desire. To address your specific needs, I propose a duration of X weeks to build, fine-tune, and train a custom Large Language Model (LLM). Considering your requirement for an optimized inference pipeline, I bring expertise in vLLM optimization which will guarantee predictions being served efficiently with low latency. Designing thorough evaluation processes is a priority for me and implementing strict Python guardrails to significantly reduce hallucinations aligns directly with my approach to informally lower error rates. Lastly, coming from the finance world, make this happen together!

@bojans9

Hi, Bojan here, from Serbia. What matters most in building your custom Python LLM for PositoAI with guardrails and evaluation is answers people can trust, at a running cost that stays predictable. I'm a senior AI developer with 8+ years of experience working with Python, Machine Learning (ML), Deep Learning and Pytorch. For your custom Python LLM, the real challenge is wrong answers and prompt injection. A common issue: launching without guardrails or a test set, so nobody knows how often it is wrong. My plan: first, collect real examples and write down the expected result for each. Then, build the Hugging Face pipeline with guardrails and logging. Finally, score it against those examples and tune it before launch. I've done AI work in Pytorch. A few questions to better understand the scope: 1. Does it need a monthly cap on model usage? Then I limit model calls and alert early, so costs never surprise you. 2. Score each change on a fixed set of real questions? I'd recommend it, quality in numbers, not impressions. 3. Will non-technical staff review its output? Then I show a short reason next to each result, so checks stay quick. I can start right away. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you, Bojan

@sharwan47

Your main goal is to replace dependency on commercial LLM APIs with a model you control, while keeping financial summaries accurate, structured, fast, and grounded in your actual stock data. I will focus on building the complete pipeline around that outcome: prepare and validate your financial dataset, benchmark suitable open-source models, fine-tune the selected model with the appropriate LoRA/QLoRA approach, add strict evaluation and hallucination checks, and deploy an optimized inference service using vLLM or a suitable alternative. The important part will be measurable output quality, not just getting a model trained. I will build evaluation datasets around your real stock-summary use cases and test accuracy, consistency, formatting and grounding before deployment. At the end, you will have a trained model, reproducible Python training pipeline, evaluation/guardrail layer, and working self-hosted inference API ready to integrate with PositoAI. I have worked on AI/API-driven application workflows and backend systems, but I will not claim direct open-source LLM training with PyTorch/Hugging Face unless that is part of the team's verified experience. If required, I can share the closest relevant AI implementation experience and technical approach before starting.

@tanvir123047

Hi, I build AI pipelines in Python and integrate models into production apps. A correction first: • "Build our own LLM" should mean fine-tuning an open model, not training from scratch (that costs millions). QLoRA on Llama 3.1 8B or Qwen 2.5 7B, on a single A100/L40S, is the right fit for structured stock summaries. Plan: 1. Data: Python pipeline turning your ratios, metrics and filings into instruction/response pairs (JSON output format). 2. Baseline: test 2-3 base models on 100 samples before training. 3. Fine-tune with QLoRA (Hugging Face TRL/PEFT), track runs, evaluate on a held-out set. 4. Accuracy: numbers come from your data via the prompt, not the model's memory, so figures can't be invented. 5. Serve with vLLM behind a FastAPI endpoint; load test and cost per 1,000 summaries. Questions: 1. How many example summaries do you have? 2,000+ good examples is a good start. 2. Cloud or own GPU? 3. Required output: fixed template, or free text? 4. What latency and daily volume do you need? Rate: ₹1,250/hr. Estimated 60-80 hours for a working, evaluated model. Tanvir

@Jafferi12

One thing to get right before any training: a fine-tuned model is frozen at its training date, and stock numbers change daily. If the model learns P/E or price from training data, it will state stale figures with total confidence. So the model should learn how to write your summaries (structure, tone, what to flag), while the live ratios get passed in at request time from your own data. That keeps it accurate and means you don't retrain every week. Plan: QLoRA on an open 7-8B model (Llama 3 / Qwen class), a Python data pipeline from your metrics and disclosures, an eval set that checks every number in a summary against the source, and vLLM serving behind an API that drops in where OpenAI sits today. How many example summaries do you already have that you'd call good?

@braing2

Hello, I built 7 products in 7 days, and every client I've worked with on Freelancer has said they'd hire me again. Duration: 3 weeks to a trained model and a working inference endpoint. Week 1: base model choice (Llama 3.1 8B vs Qwen 2.5 7B, scored on 100 of your stocks) and the data pipeline. Week 2: QLoRA fine-tune on a rented GPU. Week 3: vLLM server, guardrails and an evaluation report. Total cost: $500 fixed. Experience: most recently I fine-tuned an open-source PyTorch model on a rented RunPod A5000 for a client: data cleaning and filtering pipeline, warm-start from a pretrained checkpoint, training to a validation plateau, then export and a deployable inference package. Your project follows the same loop with Hugging Face Transformers, PEFT and TRL. The guardrail I'd put first: every number in a generated summary is checked against your source data, and a summary that states a ratio or figure that isn't in the input is rejected and regenerated, so the model can't invent a P/E. Evaluation is a fixed test set of stocks scored on numeric accuracy, coverage of required sections and format, compared with your current API output. Amul

@Nayeem1217

With 10+ years of experience in Python, Machine Learning, and Deep Learning, I can build and fine-tune an open-source LLM for accurate financial stock summaries using your data. I’m experienced with PyTorch, Hugging Face, PEFT, TRL, Accelerate, vLLM, DeepSpeed, and AWS/GPU environments. I’ll deliver an optimized training and inference pipeline with proper evaluation, guardrails, and reliable deployment. I focus on performance, accuracy, scalability, and staying within your budget and timeline.

@shailender18

Greetings, I see you're looking to replace commercial LLM APIs with your own fine-tuned open-source model that writes accurate, structured stock summaries for PositoAI.com. The aim is a reliable, low-latency model that stays faithful to your financial data. With 5+ years of Python development, I would first compare Llama 3.1 8B and Mistral 7B on a sample of your data, then fine-tune the better one with QLoRA using PyTorch, Hugging Face Transformers, PEFT and TRL. I would build the dataset pipeline that cleans and structures your ratios, metrics and disclosures into training pairs, and the model would write each summary only from that structured record. To keep hallucinations out, I would add a guardrail that extracts every number from the generated text and checks it against the source data, with regeneration or flagging on any mismatch. I would also track accuracy on a fixed test set after each run. For serving, I would deploy with vLLM behind a simple API, using quantisation and batching for low latency. I'll provide the trained model, data pipeline, evaluation reports, deployment scripts and clear setup notes. GPU rental costs on RunPod or similar are separate and paid directly by you. I'm excited about the opportunity to build this with you. Best Regards, Shailender

@ronakkataria1

Fine-tune a model on stock summaries and it learns the whole shape of a write-up, numbers included. Ask about a company and it writes figures with full confidence: a P/E from last year, a margin from another quarter, a growth rate that's slightly off. The prose is clean, so nobody catches it, and in stock research that's the error that costs trust. So numbers never come from the model's memory. Each request carries that stock's figures from your database, the model is trained to write around the numbers it's given, and a checker compares every number in the output with the source before anything publishes. A mismatch blocks the summary. Training pairs pass the same checker, since summaries your current APIs wrote can carry slips too, and a model trained on them learns the slips. Plan: a 7 to 8B open model like Llama or Qwen, LoRA with PyTorch, PEFT and TRL, an eval set scored against your source data, then vLLM serving with the checker in front. Your three answers. Duration: 6 weeks. Cost: about 120 hours at my rate, so ₹1,14,000 in total, capped. Experience: LoRA and PEFT fine-tuning on Hugging Face Transformers and PyTorch, inside an LLMOps platform I built for fine-tuning and deploying enterprise LLMs, plus RAG in production on LLaMA, Qwen, GPT-4 and Claude. My rating here is 5 out of 5. Self-hosting beats API pricing only while the GPU stays busy. Are summaries written in batches, say after each results filing, or live when a user opens a stock? Ronak

@abdurrehman373

To build a robust financial summarization engine for PositoAI, I propose a workflow starting with the selection of a highly efficient base model like Mistral or Llama 3. Given the requirement for high accuracy in financial reporting, my approach focuses on QLoRA fine-tuning on a cleaned dataset of financial metrics to ensure the model adheres strictly to your domain data while minimizing hallucinations. I have extensive experience with production-level Python and the Hugging Face stack, including PEFT and TRL for efficient training. I am comfortable setting up inference pipelines using vLLM to maintain low latency on GPU-accelerated cloud infrastructure (such as RunPod or Lambda Labs). 1. Duration: I estimate 6–8 weeks to deliver a fully trained, fine-tuned model and a deployed inference pipeline. 2. Total Cost: Based on the scope of data pipeline construction, fine-tuning, and deployment, my fixed fee for this project is 450,000 INR. 3. Experience: My background involves building Python-based data pipelines and leveraging PyTorch/Transformers to implement LLM architectures. I focus on creating scalable, maintainable AI infrastructure that avoids reliance on third-party API costs. Could you provide details on the current volume and format of your financial dataset?

@marvind18

You need a self-hosted model pipeline that produces evidence-based stock summaries from your own financial data, rather than a costly API wrapper that can hallucinate without traceability. I build production Python and AI systems: at Marin Software, I delivered serverless Python, AWS, and LangChain agent pipelines, including real-time processing and AI-assisted workflows. For PositoAI, I would separate structured financial facts from generated narrative, require every summary claim to map back to supplied metrics or disclosures, and build evaluation datasets before accepting a fine-tuning result. The deployment path should include reproducible dataset preparation, versioned model artifacts, constrained output schemas, inference monitoring, and a benchmark against a held-out set. I need to be direct: my stated background supports Python, AWS, LangChain, RAG, and AI integration, but it does not include a prior production claim of PyTorch/Hugging Face open-source LLM fine-tuning with LoRA/QLoRA. I would not fabricate that experience or quote a fixed delivery commitment before inspecting your data, GPU target, model size, accuracy bar, and licensing requirements. Can you share sample input data and the exact schema of a successful financial summary?

@abhilashr53

I can deliver 'Custom Python LLM Creation for PositoAI' end to end — clean architecture, tests, and deployment. I'll start with a short plan, share progress daily, and hand over production-ready code. Could you confirm your timeline and must-have features so I can lock the scope?

@satyamkumar16

Hello, I’m an AI/ML Engineer focused on Python, NLP, RAG, LLM applications, and Agentic AI systems. I’m interested in building the custom financial LLM pipeline for PositoAI. ### What I can deliver * Select an appropriate open-source base model based on your financial use case, latency, GPU budget, and licensing requirements. * Build a Python data-processing pipeline for financial metrics, reports, disclosures, and structured training data. * Prepare instruction/chat datasets suitable for supervised fine-tuning. * Implement LoRA/QLoRA fine-tuning using Hugging Face Transformers, PEFT, TRL, PyTorch, and Accelerate. * Build an evaluation pipeline focused on factual accuracy, structured outputs, and hallucination reduction. * Deploy the resulting model using an efficient inference stack such as vLLM or Ollama, depending on your infrastructure. * Add validation/guardrail layers to ensure generated summaries remain grounded in the supplied financial data. * Provide reproducible Python scripts, configuration files, documentation, and deployment instructions. ### Relevant Experience I have hands-on experience developing Python-based ML and AI systems using Hugging Face, LangChain, LangGraph, RAG pipelines, vector databases, local LLMs through Ollama, and deployed ML applications. I have also worked on an AI Farmer Copilot combining computer vision, RAG, weather/context information, and LangGraph-based orchestration.

@Jeeldhola01

Hi, PositoAI’s goal is clear: build a financial LLM you own, with better control over accuracy, cost, and deployment instead of depending on commercial APIs. I can help build the complete Python ML pipeline—from financial data preparation and model selection to LoRA/QLoRA fine-tuning, evaluation, hallucination guardrails, and optimized self-hosted inference. The focus will be on one thing: generating structured and reliable stock summaries grounded in your financial data. You’ll receive a reproducible training pipeline, fine-tuned model, optimized inference API, evaluation framework, deployment setup, and complete technical documentation. I’d begin by benchmarking suitable open-source models against your real stock-summary examples, so we choose the right model before spending heavily on GPU training. Ready to discuss the dataset and current PositoAI architecture.

@iFixNow

Hi, there The hard part of a self-hosted finance LLM isn't training, it's stopping fluent summaries with wrong numbers. A QLoRA-tuned 7-8B model learns tone and structure well, but it should never calculate a P/E itself. Python should compute every metric, feed it in as structured input, and check each figure in the output before users see it. The other trap is cost: an idle 24/7 GPU can outspend the API you're leaving unless it's quantized and sized to real traffic. As CTO of Assist A Boss, I build AI integrations, chatbots and agents on OpenAI models and run them on our own Linux and cloud servers, so I know where API bills and hosting costs actually land. As Windows 11 Migration Specialist for Leidos, I moved 2,500+ machines with zero data loss because no device passed until a PowerShell validation script confirmed it. That same "verify before release" rule is how I'd build your guardrails. - Apache-licensed base like Qwen or Mistral, no license limits - Clean ratios and filings into instruction pairs, QLoRA on one rented GPU - vLLM with AWQ quantization plus a number-matching guardrail If you're looking for scalable, practical value rather than just a one-time project, I can help you achieve meaningful results through small but lasting improvements that will exceed your expectations and surprise everyone involved. William Berry

@shyam351999

Hi, I am excited to propose my services for the Custom Python LLM Creation for PositoAI. With over 3 years of experience in developing robust solutions, I am well-equipped to build and train a tailored Python LLM that meets your specific needs. My approach will focus on understanding your requirements and leveraging the latest machine learning techniques to create a model that enhances user engagement and delivers accurate results. I will ensure the model is optimized for performance and scalability, aligning with PositoAI's vision. Key deliverables include: - Comprehensive analysis of your data and requirements to inform model design. - Development of a custom Python LLM using state-of-the-art libraries. - Rigorous training and fine-tuning of the model to achieve optimal performance. - Implementation of best practices for deployment and integration into your existing infrastructure. - Ongoing support and adjustments based on user feedback and performance metrics. I am committed to delivering a high-quality solution that drives results for PositoAI. Let’s collaborate to bring your vision to life. Best regards, Shyam Prajapati