| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-Coder-32B-Instruct |
| tags: |
| - code |
| - gguf |
| - qlora |
| - local-first |
| - verified-inference |
| pipeline_tag: text-generation |
| library_name: gguf |
| --- |
| |
| # Flywheel-Local-Coder-32B |
|
|
| A 32-billion-parameter coding model in a single Q4_K_M file that runs entirely |
| on your own machine. It takes Qwen2.5-Coder-32B-Instruct and continues its |
| pretraining on the same 66-million-token corpus behind the 14B, drawn from a |
| real working development ecosystem, then packs the merge into one GGUF just |
| under 19 GB. Your prompts and your code never leave your disk. And if you ever |
| care to look, the whole build, corpus to weights, can be retraced hash by hash. |
|
|
| This is the larger sibling of [Flywheel-Local-Coder-14B](https://huggingface.co/zaindanaharper/flywheel-local-coder-14b): |
| more capacity, the same local-first stance and the same retraceable chain. |
|
|
| ## Run it in two commands |
|
|
| ``` |
| hf download zaindanaharper/flywheel-local-coder-32b telos-coder-32b-cpt2019-q4_k_m.gguf --local-dir . |
| llama-cli -m telos-coder-32b-cpt2019-q4_k_m.gguf -cnv |
| ``` |
|
|
| Prefer Ollama? Download the repo folder so the GGUF and the Modelfile sit |
| together, then: |
|
|
| ``` |
| ollama create flywheel-local-coder-32b -f Modelfile |
| ollama run flywheel-local-coder-32b |
| ``` |
|
|
| No conversion step, no shards, no Python environment. The [usage guide](usage.md) |
| covers chat, deterministic completion, an OpenAI-compatible local endpoint, and |
| how to verify your download against the published checksums. |
|
|
| ## Specs at a glance |
|
|
| | | | |
| |---|---| |
| | Parameters | 32.5B (qwen2 architecture) | |
| | Context length | 32,768 tokens | |
| | Quantization | Q4_K_M, single GGUF file | |
| | File size | 18.5 GB (19,851,336,480 bytes) | |
| | Capabilities | chat, code completion, tool calling | |
| | Base model | Qwen2.5-Coder-32B-Instruct | |
| | Training | QLoRA continued pretraining, 66.2M tokens across 17,997 files | |
| | License | Apache-2.0 (with Qwen attribution) | |
| | SHA-256 | `65e6133fbe4d12579a776047a71bebb98ab86f9e3d343ed821b51dac0ce312f4` | |
|
|
| Full details in the [spec sheet](SPECS.md). |
|
|
| ## What to expect |
|
|
| This is a local-first coding companion at 32B scale: completions, functions, |
| refactors, and tool-calling on your own hardware, with your code staying home. |
| It needs more room than the 14B, roughly 20 GB of memory for the weights plus |
| context, so it is happiest on a 24 GB GPU or a machine with generous RAM; see |
| the [spec sheet](SPECS.md) for hardware guidance. |
|
|
| We publish measurements, not adjectives, and we are precise about which we have. |
| Unlike the 14B, this model does not yet carry benchmark scores. Its only |
| recorded behavioral receipt today is a deterministic generation smoke: at |
| temperature 0 with a fixed seed, reruns are byte-identical. We make no |
| capability-uplift claim over the base model. Benchmarks are pending and will |
| ship with the JSON they came from and the method to re-run them, exactly as the |
| 14B's [benchmarks page](BENCHMARKS.md) already does. Until then, the honest |
| statement is: a verified, retraceable continued-pretraining build, not a scored |
| one. |
|
|
| ## Also in this repo |
|
|
| - `telos-coder-32b-cpt2019-q4_k_m.gguf`: the runnable merged model (above). |
| - `telos-coder-32b-cpt2019-lora.gguf`: the QLoRA adapter (268 MB). Apply the |
| trained delta to your own copy of the base, or requantize from it at another |
| precision. The base weights themselves are never republished here. |
|
|
| ## The documents |
|
|
| - [Usage guide](usage.md): run it with Ollama, llama.cpp, or as a local API. |
| - [Benchmarks](BENCHMARKS.md): what is measured so far, and what is still pending. |
| - [Spec sheet](SPECS.md): hardware guidance, training details, formats. |
| - [Safety and claims](safety.md): what this model does and does not claim. |
| - [Model card](MODEL_CARD.md): the full technical card. |
| - [provenance.json](provenance.json) and [checksums.sha256](checksums.sha256): |
| the retraceable chain from corpus to the exact bytes you downloaded. |
|
|
| ## License and attribution |
|
|
| Apache-2.0. Built on Qwen2.5-Coder-32B-Instruct by the Qwen team; see LICENSE |
| for the attribution notice. The training corpus is proprietary to the author; |
| the shipped weights carry no third-party code beyond the base model, and the |
| base model is never republished on its own. |
|
|
