LLM Quantization

AI and machine learning skill, available on Zeplik

LLM Quantization is a ready-to-run AI and machine learning skill on Zeplik. Compressing and accelerating LLMs — AWQ, GPTQ, GGUF/llama.cpp, bitsandbytes, HQQ, plus distillation, pruning, model merging, FlashAttention and speculative decoding. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The LLM Quantization skill loads automatically when your request matches it, or you can invoke it directly by typing /llm-quantization in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the LLM Quantization skill can do

Try these prompts on Zeplik

Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.

How the LLM Quantization skill works

/llm-quantization

Umbrella for model compression and inference acceleration. The user wants a smaller or faster model; establish the target (memory, latency, hardware) and acceptable accuracy loss, then pick the technique and deliver code. For serving the result at scale route to llm-serving.

Dispatch table

Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.

TopicRead
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and mini…references/awq-quantization.md
GGUF format and llama.cpp quantization for efficient CPU/GPU inference.references/gguf-quantization.md
Post-training 4-bit quantization for LLMs with minimal accuracy loss.references/gptq.md
Half-Quadratic Quantization for LLMs without calibration data.references/hqq-quantization.md
Compress large language models using knowledge distillation from teacher to student mod…references/knowledge-distillation.md
Merge multiple fine-tuned models using mergekit to combine capabilities without retrain…references/model-merging.md
Reduce LLM size and accelerate inference using pruning techniques like Wanda and Sparse…references/model-pruning.md
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory…references/optimizing-attention-flash.md
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.references/quantizing-models-bitsandbytes.md
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahe…references/speculative-decoding.md

How to work

  1. Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
  2. Read the matching reference file(s) before answering. Read at most 2-3 per turn.
  3. Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
  4. Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.

Usage

/llm-quantization $ARGUMENTS

How to use the LLM Quantization skill

  1. Sign in to Zeplik

    Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the LLM Quantization skill right away.

  2. Describe your AI and machine learning task

    Ask in plain language, or type /llm-quantization to invoke the skill directly. Zeplik recognizes the LLM Quantization skill and applies its method.

  3. Review and refine the result

    Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author
davila7 (D7 umbrella-consolidation)
License
MIT

Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the LLM Quantization skill?
LLM Quantization is a ready-to-run AI and machine learning skill on Zeplik. Compressing and accelerating LLMs — AWQ, GPTQ, GGUF/llama.cpp, bitsandbytes, HQQ, plus distillation, pruning, model merging, FlashAttention and speculative decoding. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use LLM Quantization on Zeplik?
Sign in to Zeplik and ask in plain language, or type /llm-quantization in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the LLM Quantization skill use?
Any model you choose. Zeplik works across every model in one chat, so the LLM Quantization skill runs on your preferred model for the task.
Where does the LLM Quantization skill come from?
The LLM Quantization skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the LLM Quantization skill cost?
Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

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LLM Quantization - AI and machine learning skill for Zeplik AI | Zeplik Chat