Tagged llm

Export OpenTelemetry traces from LLM apps and agents to Confident AI

Add DeepEval tracing with @observe to LLM apps and agents for Confident AI

Build LLM-as-judge evals with pairwise comparison, rubrics and bias checks

Decide whether an LLM fits a task and estimate token costs for pipelines

Fine-tune LLMs with LoRA and QLoRA, including dataset prep and tuning

Run an automated research review loop with any OpenAI-compatible LLM API

Write an interview prep cheat sheet on an ML or LLM topic, with formulas and code

Write optimized prompts for a specific AI tool such as Midjourney, Cursor or an LLM

Apply LLM alignment practices for RLHF, DPO and instruction tuning

Compress LLMs to 4-bit with AWQ for faster inference with minimal accuracy loss

Fine-tune 100+ LLMs with Axolotl YAML configs, LoRA and preference methods

Pretrain large LLMs across many GPUs with torchtitan and 4D parallelism

Program LLM pipelines declaratively with DSPy and optimize prompts automatically

Fine-tune LLMs with TRL using SFT, DPO, PPO, GRPO and reward models

Apply GPTQ 4-bit post-training quantization to large LLMs with little accuracy loss

Constrain LLM output with regex and grammars to guarantee valid JSON or code

Quantize LLMs to 2, 3 or 4 bits without calibration data using HQQ

Implement, train and fine-tune Llama, Gemma, Phi and Qwen models with LitGPT

Extract structured data from LLM responses with Pydantic validation and automatic retries

Build LLM apps with LangChain agents, chains, tool calling and RAG retrieval

Trace, evaluate and monitor LLM applications with LangSmith datasets and tests

Run LLM inference on CPUs, Apple Silicon and consumer GPUs with llama.cpp

Fine-tune 100+ LLMs with LLaMA-Factory WebUI, QLoRA and multimodal support

Filter LLM inputs and outputs for unsafe content with the Llama Guard moderation model

Prune LLMs with Wanda and SparseGPT to reach high sparsity without retraining

Curate LLM training data on GPUs with deduplication, quality filters and PII redaction

Run LLM benchmark suites with 100+ tasks on Docker, Slurm HPC or cloud platforms

Add jailbreak, PII and toxicity guardrails to LLM apps with NeMo Guardrails and Colang

Trace, evaluate and monitor LLM apps with the open-source Phoenix observability tool

Quantize LLMs to 8-bit or 4-bit with bitsandbytes to cut memory use

Serve LLMs with vLLM for high-throughput, OpenAI-compatible inference APIs

Serve LLMs with SGLang for structured JSON, regex output and prefix-cached inference

Align LLMs with SimPO, a reference-free alternative to DPO that trains faster

Post-train LLMs with reinforcement learning using slime on Megatron and SGLang

Speed up LLM inference with speculative decoding, Medusa heads and lookahead decoding

Optimize LLM inference on NVIDIA GPUs with TensorRT-LLM for high throughput

Train very large LLMs with Megatron-Core tensor, pipeline and expert parallelism

Train LLMs with RLHF, GRPO and PPO at scale using the verl framework

Add Olakai monitoring to an existing LLM app with custom KPIs and validation

Query several LLM models in parallel and compare their answers side by side

Fine-tune LLMs with LoRA or QLoRA adapters and JSONL datasets using Hugging Face PEFT

Write, refactor and evaluate LLM prompts with output schemas and test suites

Trace and evaluate LLM apps with the Phoenix observability platform

Write code or LLM-as-judge evaluators that score agent and pipeline outputs

Find failure patterns in LLM agent traces with open coding and annotations

Get and configure free API keys for LLM providers with free tiers

Build a persistent Markdown wiki from sources that you can query and lint

Fine-tune language and vision models with TRL or Unsloth on cloud GPUs

Post-train LLMs with SFT, DPO, GRPO, KTO and reward models using the TRL library