Hey everyone! I'm super excited to see Llama 3 released! 🎉 As a beginner, I'm trying to learn how to properly compare these large models.
I've been using GPT-4 for some basic scripting and learning DevOps concepts. Now I want to evaluate Llama 3 to see where it shines. Could you share your plans or any beginner-friendly frameworks?
I was thinking of testing them on tasks like:
- Explaining a Dockerfile
- Writing a simple Terraform config for an AWS EC2 instance
- Debugging a bash script
But I don't know how to structure the tests or score the outputs fairly. What tools or rubrics are you all using? I've heard of HELM and MT-Bench but they seem advanced.
Thanks for any guidance!
I work at a small software agency, and we use GPT-4 via the API for generating documentation and internal admin scripts.
My plan to evaluate Llama 3 is based on four practical things:
1. **Local cost control** - Running Llama 3 locally costs $0 in API fees. The tradeoff is needing your own hardware; a basic test with the 8B model needed about 10GB of RAM and a decent CPU on my M1 Mac.
2. **Task accuracy on internal docs** - For our use case, I'll test each model on five of our real, messy internal runbooks. I'll score them 1-5 on whether the instructions they generate would actually work for a junior dev without corrections.
3. **Speed for batch jobs** - When we process multiple documents, throughput matters. In my quick test, GPT-4 via API was faster for single questions, but running Llama locally could process a batch of 50 small tasks without rate limiting, which is a win for scheduled jobs.
4. **Setup and maintenance** - GPT-4 is just an API call. Getting Llama 3 running locally took me about 90 minutes of setup (downloading, picking a tool like Ollama, troubleshooting libraries). That's a real time cost.
I'd recommend sticking with GPT-4 for now if you're a beginner and your main use is learning and scripting. Its answers are generally more reliable out of the gate. If you want to pick Llama 3, you should tell us your budget for API fees and whether you have a spare machine to run it on consistently.
Still learning.
> GPT-4 is just an API call.
That's the core of it for a small team. Your 90-minute setup time is a real project cost, and it's a repeating one when you need to update or troubleshoot the local stack.
If you do proceed with the batch job testing, check how Llama 3 handles variable substitution in those admin scripts. I've seen models sometimes use the wrong syntax between `$VAR`, `${VAR}`, and `$(command)`, which silently breaks the script. That's a specific failure point GPT-4 usually gets right.
Build once, deploy everywhere
Your three test ideas are solid for a beginner. Start with those exact tasks and use the same prompt for both models. Scoring is the tricky part.
For the Terraform config, run `terraform plan` on each output and see which passes validation. For the bash script debugging, execute both suggested fixes in a sandbox and compare the error output.
Don't worry about HELM benchmarks yet. Your practical tests will give you a better feel for which model fits your workflow. The real question is whether Llama 3's output is good enough to justify the zero API cost for your learning projects.
Oh, running `terraform plan` on the outputs is a great concrete test! I wouldn't have thought of that. Makes scoring way more objective than me just guessing which config looks better.
For a beginner, setting up a safe sandbox for the bash script tests sounds a bit daunting though. What's the easiest way to do that without messing up my local machine? A throwaway Docker container maybe?
Great starting list! For the Dockerfile explanation task, I'd add a specific wrinkle to really test understanding. Ask both models to not just explain, but also suggest one improvement for a multi-stage build and explain why it's better. That pushes beyond surface-level description.
I actually just did a similar comparison for my team's internal knowledge base. We found GPT-4 was slightly more consistent on edge-case bash syntax, but the quality gap for straightforward DevOps explanations was way smaller than expected. Llama 3 70B held its own.
For scoring, I kept a simple spreadsheet with pass/fail on three criteria: was the answer factually correct, was it clear for a new hire, and did it include the requested specific element? No fancy rubric needed.
K8s enthusiast
For a beginner, your test list is fine. The key is using the exact same prompts for both models.
Terraform plan validation is a good objective score. For bash, use a disposable container. `docker run --rm -it ubuntu bash` works. Execute the broken script and each model's fix suggestion there. It's isolated and you can just delete it.
Ignore the advanced benchmarks. Your practical tests will tell you if the zero API cost is worth the slight accuracy trade-off for your learning use case.
Beep boop. Show me the data.