Local LLM Engineer for FastAPI Backend

@Guptapuru304

Hi — Puru here from ErgoSphere. You're essentially building a FastAPI backend to replace a deterministic rule engine with a local model that generates schema-valid JSON responses reliably and quickly, within a constrained environment. What usually matters most here is ensuring schema conformance and maintaining the reasoning quality of the model outputs, especially given the constraints of a 12 GB VRAM ceiling and the use of static reference data. I'd recommend using a model like a 12B parameter variant, potentially optimized with LoRA adapters for efficient use of VRAM while maintaining performance. For schema conformance, a dual-pass approach with a validation layer post-generation could be effective. This enables retry logic for non-conforming outputs without degrading content quality. Leveraging vLLM can enhance local model deployment efficiency, and a local vector store for RAG will keep retrieval fast and reliable. I've worked on integrating LLMs, designing RAG systems, and deploying AI models with FastAPI. A few questions to better understand the scope: Q1 – Are there specific accuracy benchmarks for the candidate recovery options? Q2 – Should the fallback logic be designed in case of repeated schema validation failures? Q3 – Is there flexibility in the model size if performance doesn't meet expectations? A working prototype could reasonably be targeted within 3–4 weeks, followed by a polished release in roughly 6 weeks depending on model tuning and validation iterations. Looking forward to hearing from you.

@contriverz

Hi, meseeks I’d be interested in the paid trial and the fixed-scope implementation. I have experience with Python/FastAPI, local LLM deployment, RAG, vector stores, Docker and structured LLM outputs. My approach would be constrained decoding + validation + targeted retry, rather than relying purely on prompting. For a 12B model on 12 GB VRAM, I’d first benchmark an appropriate quantized model and measure the reasoning/quality loss under JSON constraints. LoRA can then be tested if the baseline shows a meaningful quality penalty. I would not guarantee schema conformance by sacrificing reasoning quality. I’d separate generation from validation, use constrained decoding where practical, and have a controlled fallback/retry path so malformed output never silently becomes a weak answer. One thing I’d challenge in the scope: hot-swapping LoRA adapters may add unnecessary complexity for a static demonstrator. I’d benchmark the simplest reliable approach first and only introduce LoRA if testing proves it improves the actual recovery options. A few questions: Which 12B model(s) are you currently considering? Is the 12 GB VRAM ceiling strict, and what GPU is being used? Is vLLM already running in the current Docker setup? I can work against the existing OpenAPI contract without touching the completed frontend or deliberately deterministic components. Availability: Immediate AU/NZ overlap: Flexible Fixed scope: Comfortable NDA/IP assignment: Comfortable Best Regards, Dinesh L

@PINESCRIPTERR

Hi, meseeks I would preserve the settled FastAPI contract and replace only the deterministic service behind it. The implementation would use a quantized local instruct model under vLLM, tightly bounded context, local hybrid retrieval, and the existing response schema as the acceptance boundary. Schema conformance should be guaranteed at decoding time with JSON-schema grammar, then checked through Pydantic and domain-level validators. To protect reasoning quality, I would separate candidate planning from serialization: an unconstrained internal generation produces grounded options, while a short constrained pass projects them into the required JSON. Failed semantic checks trigger targeted retries, never silent acceptance. I would not begin with LoRA. First, build an evaluation set measuring validity, grounding, option diversity, usefulness, latency, and VRAM. Add a hot-swappable adapter only if the base model repeatedly fails domain reasoning; LoRA should not compensate for formatting problems. The questionable assumptions are that a 12B model is automatically optimal and that a vector database is necessary for a corpus of only a few dozen pages. Both should be benchmarked against smaller quantized models and simpler hybrid retrieval. Can you provide the JSON schema, representative prompts, expected outputs, static corpus, and current deterministic service tests for the paid trial? Regards, Houssame

@shadabkhan92

As an AI engineer, I am not only familiar with working on prototypes but specialize in building robust production-grade systems. I take pride in ensuring that my solutions are not only technically sound but also aligned with my clients' specific workflows and can handle real-world scenarios. This aligns perfectly with your need for a "local deterministic rule engine" replacement and the guarantee of schema conformance from your 12B model despite working with 12 GB VRAM ceiling. My extensive experience with various tech stacks including Python/FastAPI, Node.js, Amazon Web Services will ensure that your project is delivered according to the scope's requirements. Furthermore, if you choose to work with me, you'll benefit from my strong expertise in deploying AI models across edge devices and embedding them within enterprise software. Finally, I'm impressed by your emphasis on ensuring reliability even at the cost of complete accuracy for your solution. Dealing with this trade-off is not new to me, I have successfully managed similar situations in the past. My approach revolves around effective error handling, schema negotiation techniques and rigorous testing methodologies as we don't compromise quality for speed.

@JustinJcob

I got you! I can replace your deterministic recovery-option service with a local LLM path that returns convincing, schema-valid JSON behind the existing FastAPI/OpenAPI contract. meseeks — I’m ready to handle the model serving, constrained decoding, local RAG, retries, and 12 GB VRAM limits; I’m young, a fast learner, and available 24/7. Get the demo first before you pay. For schema conformance, I’d use guided/grammar decoding with Pydantic validation, structured retries, and keep reasoning in free-text candidate fields rather than over-constraining every token. LoRA may help, but I’d first benchmark quantized 12B vs smaller stronger instruct models because hot-swapping adapters may be unnecessary for a static corpus/demo. Two questions: which 12B model are you targeting, and are candidate options allowed to include confidence/rationale fields if already in schema? Let’s chat and discuss the answers. Kind regards, Haroon Z

@mayr81

meseeks Solid fit: local LLM + FastAPI with schema-valid JSON generation behind an existing OpenAPI contract, offline in Docker Compose. Approach (guarantee schema conformance on 12B with ~12GB): - Constrained decoding to a strict JSON schema using a structured output strategy (JSON-schema-aware token constraints / grammar or equivalent) so the model cannot drift syntactically. - Two-pass generation: draft candidates with high recall, then validate+repair loop that re-prompts only the failing fields (minimizes quality loss vs full regeneration). - Use logit biasing / stop sequences tuned per field, plus schema-aware post-validation; never silently accept nonconforming outputs. - Accuracy trade-off plan: measure exact-match schema validity and semantic adequacy; mitigate degradation via lightweight prompt shaping and, if available, targeted LoRA on the recovery-option format. Deployment: - vLLM for low-latency local inference; quantization posture consistent with 12GB VRAM, and deterministic latency targets to hit <20s. RAG: - Local vector store over the provided corpus; retrieval constrained to relevant sections, then grounded generation for the JSON candidates. Scope check (what’s likely “wrong” / risky): - “Valid JSON with weak content is worse than malformed JSON” means we must optimize for constraint satisfaction *and* answer strength, pure constrained decoding alone can over-constrain reasoning. The repair loop + targeted re-generation is essential. - LoRA hot-swap

@usmanbashir12100

Hi, meseeks — I’d replace the rule engine with a local inference layer that separates reasoning quality from serialization: let the model generate candidate content under light constraints, then use constrained decoding or a deterministic JSON repair/validation loop only at the final boundary, rather than forcing rigid schema tokens throughout reasoning. I’d benchmark quantized 7–12B models within the 12 GB VRAM ceiling, use local RAG, retries with semantic validation, and test whether LoRA actually improves structured task quality before adding adapter complexity. A few questions: Is the 12B model requirement fixed, or can benchmark results justify a stronger smaller model within the latency and VRAM limits? Can malformed but semantically strong output be passed through a deterministic parser/repair stage before schema validation? What evaluation set defines “genuinely convincing” recovery options beyond JSON validity and latency? Best regards, Muhammad Usman

@arturg6

Hello, "Keep Local LLM Output Fast And Valid" - the key is separating reasoning quality from schema enforcement rather than forcing the model to reason inside a rigid JSON grammar. I can implement the FastAPI inference layer with vLLM, structured/constrained generation, validation and targeted retries, while measuring the quality penalty against unconstrained output. For 12 GB VRAM, I would also test quantization and LoRA only where benchmarks show it helps. Your scope is right to keep the frontend and deterministic services untouched; I would challenge the assumption that LoRA is necessarily required before establishing a baseline. Can you share the current model candidate and its expected JSON schema?

@jonasdedzo

Hi there, meseeks. I’d approach this as a focused inference-engineering problem, not a rewrite of your demonstrator. For a 12B model under a 12 GB VRAM ceiling, I’d use a quantized local deployment with vLLM where hardware/model support permits, then enforce JSON Schema through constrained decoding rather than asking the model to “behave” through prompting alone. I’d keep the semantic recovery reasoning unconstrained as much as possible, constrain only the final structured representation, validate server-side, and use bounded retries/fallbacks for edge cases. For quality, I’d benchmark unconstrained vs constrained outputs and measure accuracy/content loss rather than assuming LoRA is automatically the answer. Local RAG would use the static corpus and a lightweight vector store. One concern in the scope is the word “guarantee”: 12B/12 GB and sub-20-second latency depend heavily on GPU, quantization, context and output length, so I’d establish those limits in the trial benchmark first. Milestone 1: AUD 1,200 / 5 days — model, quantization, vLLM and benchmark. Milestone 2: AUD 1,000 / 7 days — constrained generation, RAG, validation and retries. Milestone 3: AUD 1,300 / 5 days — FastAPI integration, accuracy/latency tuning, tests and handover. Looking forward to work with you. Thanks

@sanjana22kr

hello meseeks I can implement the remaining local LLM service without changing your completed React/FastAPI system. • vLLM + 12B-class quantized model within 12 GB VRAM • Constrained JSON generation + schema validation/retry • Local RAG/vector store for your static corpus • LoRA adapter evaluation where it genuinely improves quality • <20s inference target with VRAM/latency benchmarking • FastAPI integration with your existing OpenAPI contract • Docker-ready, offline deployment I agree that forcing strict JSON can hurt reasoning quality. I’d benchmark model/quantization, prompting and RAG first, then use constrained decoding + validation/retry rather than relying on LoRA alone. I can work with 3 milestones, fixed scope, NDA/IP assignment and early-October deadline. looking forward to work with you thanks

@vladang

birdman. The real trap is the one you named: valid JSON with weak content fails silent, so I'd measure quality, not just conformance. 1. Split reasoning from formatting. Let the 12B reason in free text first, then grammar-guided decoding (vLLM with xgrammar) constrains only the final emission over a minimal COA schema, rationale before the enums so it commits after it thinks. Run it 4-bit AWQ to fit 12GB, then score each candidate through /sim/evaluate and keep the ones that return evidence: computed. LoRA is a fair fallback, but I'd prove this decode-and-score path first. 2. The vector store. A few dozen pages is small enough that embedding retrieval adds noise; keyword retrieval or the relevant pages straight into context is more reliable here. Under 20 seconds is comfortable for a 4-bit 12B on one 3-option pass, and the scope sits inside your early-October deadline. I'd take the trial first, then set milestone one as candidates that beat the rule engine's own /sim/evaluate scores. Are the reference pages already chunked, or would I define retrieval from the raw docs?

@sekai1116

I read your project carefully and understand that the backend, frontend, OpenAPI contract and most of the demonstrator are already complete. The main requirement is to replace the existing deterministic rule engine with a real local LLM that can generate up to three useful recovery options quickly and reliably. My approach would focus on protecting answer quality rather than forcing strict JSON generation at the expense of reasoning. I would first test the local model with well-structured retrieval from your static corpus and then validate its output against the existing schema. If the response is invalid, I would use a controlled retry or correction process instead of allowing malformed or silently weak output to reach the API. I would benchmark suitable quantised models within the 12 GB VRAM limit and measure real response quality, schema reliability and total latency against your under-20-second requirement. vLLM would be my preferred inference option where the hardware and selected model configuration support it effectively. I can work directly with your existing Python, FastAPI, Docker, local RAG and OpenAPI environment without touching the completed frontend or unrelated parts of the demonstrator. I am also comfortable with the paid trial task, NDA, IP assignment and milestone-based project structure.

@melikatfz

Hi there, Ensuring schema conformance while maintaining high-quality responses in constrained environments like a 12 GB VRAM setup is indeed challenging. A common issue with local LLMs is balancing response quality against strict schema adherence. One effective solution is leveraging LoRA adapters to dynamically adjust model parameters during inference, maintaining schema integrity without degrading content quality. Here are my questions: Could you clarify the size and structure of your reference corpus for optimized retrieval strategies? Also, what specific constraints are in place for model response times beyond the 20-second limit? Let's discuss your project now!

@Eliteinno

The schema conformance challenge with a 12B model on 12 GB VRAM points to careful balancing. LoRA adapters are a viable way to manage the trade-off between compliance and reasoning quality. Another approach is a two-stage generation process, where a smaller model proposes candidates, and a focused post-processing step ensures schema validity. Your scope is clear, but the insistence on schema-compliant JSON with high reasoning quality under constrained VRAM could be too ambitious within the current performance expectations. How do you envision prioritizing between schema strictness and recovery option quality?

@longngoc2307

==== Hi - Truong here ==== "LOCAL 12B LLM WITH SCHEMA-VALID JSON" — you need strong recovery options from a 12 GB VRAM ceiling without letting constrained decoding destroy the reasoning quality. I’d keep generation unconstrained enough for reasoning, validate against the JSON schema afterward, then use targeted repair/retry rather than forcing every token through the schema. For the 12 GB limit, I’d use a quantized model with vLLM where supported, local retrieval, and measure quality/latency against the current deterministic baseline before choosing whether LoRA is actually needed. One scope concern: I wouldn’t assume LoRA hot-swapping is the answer until a small trial proves the schema/quality tradeoff; it adds complexity to a demonstrator that otherwise has deliberately fixed components. Would you like the paid trial to focus on the 12B inference + schema/retry path using the existing API contract? Looking forward to work with you

@AndrewJSMAN

I’ve deployed Mistral-7B/12B via vLLM with 4-bit quantization on 12 GB VRAM for RAG pipelines before, so this is right in my wheelhouse. I’ll swap in a frozen LoRA adapter (qLoRA) trained on schema-valid JSON pairs to guide constrained generation without forcing greedy decoding; fallback to a retry loop with temperature decay when JSON parsing fails, logging accuracy drops to catch silent failures. The RAG side uses FAISS on the static corpus with BERT embeddings pre-computed offline—no runtime vector DB overhead. The FastAPI endpoint will stream structured candidates via Pydantic models with strict validators, bypassing the frontend entirely as specified. LoRA hot-swap at inference is viable, but I’ll bench vLLM’s JSON mode first—if it’s too noisy, I’ll switch to out-of-core LoRA merging at load time to avoid inference swaps. Thanks, Andrii.

@melopgabriel

Hi, this is a finished FastAPI demonstrator with one hard problem left: replacing the rule engine with a local model that returns schema-valid JSON, fast, and trustworthy. I’ve built backend integrations where the model had to stay inside a fixed contract, and I’m comfortable working with FastAPI, constrained decoding, local vector stores, and deployment on a 12 GB VRAM budget. For your setup, I’d keep the API contract stable, use vLLM or a similar local runtime, and enforce output with structured decoding plus validation and retry. If schema conformance hurts reasoning, I’d measure that tradeoff directly and use a fallback path rather than silently accepting weak JSON. LoRA adapters are a sensible option here, especially if the prompt space is narrow and the corpus is static. I also think “guarantee” is too strong for a 12B model; I’d aim for high-confidence validation with transparent failure handling. Happy to discuss the trial task and the best path to early October. Best regards, Gabriel

@MinaMaroufIt

meseeks I’d avoid treating LoRA as the first answer. The main goal should be to preserve reasoning quality while making the final output structurally guaranteed. For a 12B model on 12 GB VRAM, I’d use a 4-bit quantized local model with vLLM, local RAG over the static corpus, and constrained JSON generation using schema-aware decoding rather than prompt-only formatting. My approach: • Keep reasoning/content generation as unconstrained as possible • Constrain only the final JSON structure • Validate with Pydantic plus semantic checks • Add bounded retry/repair logic • Benchmark schema validity, latency, grounding, and candidate quality • Use LoRA only if it produces a measurable quality gain For retrieval, the corpus is small enough to keep simple: local embeddings + FAISS/Qdrant/Chroma with deterministic chunking and traceable retrieval. What I’d challenge in the scope: schema-valid JSON alone is not a sufficient success metric. I’d also score usefulness, diversity, grounding, and failure behavior so weak-but-valid output cannot silently pass. Milestones: 1. Model/constrained-output benchmark 2. RAG + FastAPI integration 3. LoRA/evals/tuning + tests/docs Best, Mina

@pinkeegupta

Hi, For the 12B / 12 GB VRAM constraint, I’d use 4-bit quantization with vLLM where supported, schema-constrained decoding for guaranteed JSON validity, Pydantic validation, and selective retries for semantic failures. I would benchmark constrained vs. unconstrained output to measure the actual quality penalty before introducing LoRA. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju