Hey folks, been lurking here for a bit but finally decided to post. I'm a junior DevOps engineer trying to really nail down my CI/CD workflows, and I rely heavily on GitHub Copilot in VSCode for writing pipeline scripts (mostly GitHub Actions and some Jenkins), Dockerfiles, and basic Kubernetes manifests.
I saw the announcement about CodeLlama 70B Instruct. Running models locally is really appealing for privacy and cost, especially when dealing with company code. Has anyone here actually tried running this specific model as a daily driver for coding, specifically for infrastructure-as-code and pipeline work?
I'm curious about practical comparisons. For example:
* How does it handle a moderately complex multi-stage Dockerfile for a Python/Node.js app compared to Copilot?
* If I give it a prompt like "write a GitHub Actions workflow that builds a Docker image, runs unit tests, and pushes to ECR on a tag," does it produce a usable YAML with proper secrets handling hints?
* What about spotting common pitfalls in `.gitlab-ci.yml` or Jenkins `Jenkinsfile` (groovy) syntax?
I've got a machine with 64GB RAM, so technically I could run the quantized versions. But before I spend time setting it up, I'd love to hear if anyone has done a head-to-head on real tasks. My main worries are:
1. Will it keep up with the context of an entire file like Copilot does?
2. Is it significantly slower in generating suggestions?
3. Does it understand the "DevOps" context well—like knowing common tools (kubectl, helm, terraform) and their typical usage patterns?
If you've tried it, what model version/quantization did you use, and what was your setup (Ollama, llama.cpp, etc.)? Any examples of it passing or failing on specific tasks would be super helpful.
Learning by breaking
I've been running the 4-bit quantized 70B instruct on a 3090 + 64GB swap, so I can speak to this. For your specific use case - infra-as-code - the results are mixed.
On the Dockerfile front: it handles multi-stage builds reasonably well, but it tends to over-engineer. It'll suggest a python-slim base for the builder stage when you don't need it, and it often misses `--no-cache` flags unless you explicitly prompt for optimization. Copilot is better at inferring the "typical" patterns from your repo context.
The GitHub Actions YAML output is actually solid. I gave it your exact prompt and it produced a usable workflow with `on: push: tags:`, matrix strategy for python/node, and a separate job for push to ECR. It even added `OIDC` comments for role-based auth. Where it falls short: it rarely suggests using `actions/cache` steps unless you ask, and it sometimes hallucinates environment variable names that don't exist in your actual AWS setup.
For `.gitlab-ci.yml` and Groovy Jenkinsfiles - it's weaker. Groovy syntax handling is spotty because the training data for Jenkins DSL is sparse. I've seen it generate `parameters` blocks with `defaultValue` as a string instead of a typed value, or miss the `agent` section entirely. You'd need to verify every line.
The real bottleneck is latency. On your 64GB RAM, with a 4-bit quantized model, you're looking at 2-3 seconds per autocomplete suggestion. For a junior dev iterating on a YAML file, that kills flow. Copilot is nearly instant.
What's your tolerance for waiting vs. the privacy benefit?