Instinct-Python-Coder-Gemma4-12B-GLM5.2

Instinct-Python-Coder-Gemma4-12B-GLM5.2 is a general-purpose Python coder that thinks briefly, then answers. We took the highly capable Gemma 4 12B model and taught it to perform better in Python coding with much more concise reasoning in its <think></think> channel, leading to faster inference, a shorter context window, and cost savings for the end user. The reasoning style is distilled from GLM-5.2: the model works through the approach in a few lines, then hands back the code.

Evaluation

We measured first-attempt accuracy on a held-out set of 228 Python tasks: one greedy completion per task, each graded automatically.

	Gemma 4 12B (base)	Instinct
Solved	40 / 228 (17.5%)	85 / 228 (37.3%)
Ran out of budget without writing code	90	1
Time to run the full set (batched)	233.6 min	51.7 min

With a 37.3% first-attempt accuracy, our Python Coder Instinct model preserved 49% of GLM-5.2's thinking capability: GLM-5.2 itself reaches about 75.6% first-attempt accuracy on a broader Python pool under the same kind of check. For comparison, the base Gemma 4 12B model has only 17.5% accuracy, so we more than doubled it.

On 90 of the 228 tasks the base model exhausted its budget without ever writing code; Instinct did that once. The same concise reasoning is why the full set evaluates 4.5x faster.

Training

Fine-tuned on 4.52M post-training tokens, passed over twice for 9.03M tokens in total, at a sequence length of 8,192.

Limitations

This is one 12B model measured once with greedy decoding, so treat 37.3% as a single reading with no error bar. It was tuned and tested on Python, and nothing else was measured here. It inherits Gemma 4's behavior and limitations.

License and lineage

Base model: unsloth/gemma-4-12b-it, which ships under the Apache 2.0 license (see its own model card's frontmatter — license: apache-2.0), not the standard Gemma Terms of Use. This fine-tune inherits that Apache 2.0 license.

Usage

It ships as a single Q8_0 GGUF, roughly 13 GB on disk, and runs on a GPU or Mac with about 16 GB of memory. Serve it with llama.cpp:

llama-server -m Instinct-Python-Coder-Gemma4-12B-GLM5.2-Q8_0.gguf -ngl 99 -c 8192

That exposes an OpenAI-compatible endpoint at http://localhost:8080/v1, and the Gemma 4 chat template is baked into the GGUF, so turns and the thinking channel are formatted for you. It runs anywhere GGUF runs, and any tool that speaks the OpenAI chat API can drive it:

• Runtimes and apps: llama.cpp, Ollama, LM Studio, Jan, KoboldCpp
• Coding agents and harnesses: opencode, pi, Hermes, Aider, Cline, Continue

Describe what you want in plain language and it replies with a short pass of reasoning followed by the code:

User: Merge two sorted lists into one sorted list.

<think>
Two pointers, take the smaller head each step, append whatever is left over. O(n+m).
</think>

```python
def merge(a, b):
    i = j = 0
    out = []
    while i < len(a) and j < len(b):
        if a[i] <= b[j]:
            out.append(a[i]); i += 1
        else:
            out.append(b[j]); j += 1
    out.extend(a[i:])
    out.extend(b[j:])
    return out
```