Usage Guide

Everything below assumes you downloaded this repo folder, so the GGUF and the Modelfile sit together in your working directory.

Verify your download (optional, 30 seconds)

sha256sum telos-coder-32b-cpt2019-q4_k_m.gguf

Compare against checksums.sha256. A match means you hold the exact bytes the provenance chain describes.

Ollama

ollama create flywheel-local-coder-32b -f Modelfile
ollama run flywheel-local-coder-32b

That gives you interactive chat. Ollama also exposes an OpenAI-compatible API the moment the model is created, so any tool that speaks the OpenAI chat format can use the model locally:

curl http://127.0.0.1:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"flywheel-local-coder-32b","messages":[{"role":"user","content":"Write a Python function that merges overlapping intervals."}]}'

Point your editor plugin, agent framework, or script at http://127.0.0.1:11434/v1 with model name flywheel-local-coder-32b and you have a private, zero-cost coding endpoint. On an 18.5 GB model, first-token latency depends on how much of the model fits in VRAM; see the spec sheet for hardware guidance.

llama.cpp

Interactive chat, one command:

llama-cli -m telos-coder-32b-cpt2019-q4_k_m.gguf -cnv

Deterministic completion (the exact configuration our receipt uses):

llama-cli -m telos-coder-32b-cpt2019-q4_k_m.gguf --temp 0 --seed 7 -n 64 -p "your prompt"

At temperature 0 with a fixed seed, reruns are byte-identical. That is not a nicety: it is what lets a receipt be re-checked by someone who is not us.

The adapter

If you would rather apply the trained delta yourself, or requantize at another precision, pull the LoRA adapter and work from your own copy of the base:

hf download zaindanaharper/flywheel-local-coder-32b telos-coder-32b-cpt2019-lora.gguf --local-dir .

The base weights are never republished here; bring your own copy of Qwen2.5-Coder-32B-Instruct (Apache-2.0) and apply the adapter with llama.cpp's --lora, or merge and requantize with llama-export-lora + llama-quantize.

Tool calling

The model supports tool/function calling through Ollama's OpenAI-compatible endpoint: pass a tools array in the request as you would with any OpenAI-style API.

Tips

Give it the full contract. Precise asks (exact exception messages, edge cases, output format) get precise answers.
Pair it with your tests. Its natural habitat is a propose-then-verify loop: let it write, run your tests, keep what passes.
32,768-token context: enough for a large file plus conversation, not an entire repository. Feed it the relevant slice.
If speed matters more than capacity, the 14B sibling runs the same way at less than half the memory.