Generative AI & LLM Engineering

High-Throughput Inference

Escaping the HuggingFace Pipeline Bottleneck

Naive model deployment leads to GPU memory fragmentation and unacceptable Time-To-First-Token (TTFT). We implement advanced serving architectures using PagedAttention and continuous batching.

By leveraging custom CUDA kernels and optimizing precision states (AWQ/GPTQ), we maximize hardware utilization, driving down the unit cost of every token generated in production.

vLLM TensorRT AWQ / GPTQ Flash-Decoding SGLang

> [INFO] Initializing vLLM Engine (GPU 0: NVIDIA H100)
> [INFO] Loading weights: meta-llama/Llama-3-70B-Instruct-AWQ
> [INFO] Quantization: AWQ (4-bit) | Precision: bfloat16
> [INFO] Allocating KV Cache (PagedAttention)...
> [INFO] Max sequence length: 8192 | Block size: 16
> [CUDA] Flash-Decoding activated for generation phase.
> [METRIC] TTFT: 142ms | TPOT: 18ms | Throughput: 4,200 tok/s
> [STATUS] Engine ready. Listening on port 8000...

Structured Generation

Forcing LLMs to Obey Neuro-Symbolic Logic

LLMs are probabilistic text generators, which makes them inherently dangerous for enterprise automation. "Prompting" them to output JSON frequently fails in edge cases.

We deploy frameworks that intervene directly at the logits level during the decoding process. By masking invalid tokens before they are sampled, we mathematically guarantee that the LLM output conforms perfectly to your required Pydantic schemas, RegEx, or SQL grammar.

Outlines Guidance DSPy LMQL

deterministic_routing.py

import outlines
from pydantic import BaseModel, Field

# Define the strict neuro-symbolic execution schema
class ExecutionPlan(BaseModel):
    action: str = Field(pattern="^(QUERY_DB|CALL_API|ESCALATE)$")
    confidence: float = Field(ge=0.0, le=1.0)
    parameters: dict

# Bind model and constrain the logits sampling deterministically
model = outlines.models.vllm("mistralai/Mistral-7B-Instruct")
generator = outlines.generate.json(model, ExecutionPlan)

# 100% Guaranteed to return a valid JSON matching the schema
result = generator("Evaluate user request: Calculate Q3 revenue.")

Alignment & Fine-Tuning

Domain Adaptation Without Catastrophic Forgetting

When off-the-shelf models fail at domain-specific reasoning (like clinical triage or financial ledger math), we align them to your proprietary data.

Using Unsloth for ultra-fast gradient computation and techniques like DPO (Direct Preference Optimization), we teach models exactly *how* to reason within your domain, minimizing hallucination while drastically reducing cloud training costs.

Unsloth LoRA / QLoRA DPO / ORPO CUDA Kernels

> [TRAIN] Initiating Unsloth QLoRA fine-tuning sequence...
> [MEMORY] Base Model: 7B | Loaded in 4-bit | VRAM footprint: 5.2GB
> [OPTIMIZE] Triton kernels enabled for RoPE and CrossEntropyLoss.
> [DATA] Loading Medical-Chain-Of-Thought Dataset (N=15,400)
> [EPOCH 1/3] Step 50/480 | Loss: 1.402 | LR: 2e-4
> [EPOCH 1/3] Step 100/480 | Loss: 0.984 | LR: 1.8e-4
> [ALIGN] Applying Direct Preference Optimization (DPO)...
> [EVAL] Reward margin increasing. Hallucination rate dropping.
> [SAVE] Exporting LoRA adapters to safetensors... DONE.