Tune LLM(Groq Model Tune Technique's)

Tune of Groq LLM, Its important to first understand:

Important clarification:

. Groq does not currently support custom model file-tunning on their platform .

Instead ,Groq provides inference-as-a-service for pretrained model like LLaMA3, mistral, Gemma etc, running at extremely high speed using their custom Groq

Since you can’t find-tune the LLMs on Groq, you can simulate it using these methods:

Option 1: Prompt Engineering + Few-Shot Learning

Embed your find-tuning knowledge directly into the prompt

Prompt =””” You are an expert AI Interview asistent

Example 1

Q: What is the vectorization in NPL?
A: Vectorization is the process of converting text into numerical form.
Q: What is embedding
A:Embadding are dense vector representation of tokens.

Option 2 : RAG(Retrieval -Augmented Generation) + Groq

You can build Rag System :

1. Stor yourinterview Q & A ,PDF, CSV in a vector database (FASIS, PINECONE etc)
2. Retrieve relevant document based on the use questions
3. Send those documents as context to Groq LLM.

This mimics tunning without touching the model.

Option 3: Use Local LLM for tuning ,Groq for Inference

If you want real fine-tuning, do this:

1. Fine-tune LLaMA or Istral on your dataset(ex: Interview dataset) locally or on cloud.
2. Then “ 2.1 Quantize the model with gguf 2.2 Deploy locally with LLM engines like Ollama,vLLM or llama.cpp 2.3 Or use Groq-compatible format in future(if supported)

Regards