---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
base_model_relation: finetune
tags:
- code
- coding-agent
- tool-use
- function-calling
- small-language-model
- full-parameter-finetuning
- supervised-fine-tuning
- deterministic-verification
- safetensors
- subroutine:search_query_gen
model-index:
- name: Code Search-Query Generator (SmolLM2 135M)
  results:
  - task:
      type: text-generation
      name: Code Search-Query Generator
    dataset:
      name: Held-out HTTPX and Jinja2 oracle benchmark
      type: custom
    metrics:
    - type: accuracy
      value: 0.648
      name: Success after one schema-feedback retry
    - type: accuracy
      value: 1.0
      name: First-pass schema validity
---

# Code Search-Query Generator (SmolLM2 135M)

This is a **full-parameter supervised fine-tune** of
[`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) for one narrow,
schema-bound developer-agent subroutine:

> Write an executable search regex from a plain-words request.

The model is one cell from the
[Parameter Floors for Developer-Agent Subroutines](https://github.com/IshaanAyaan/slm-agents)
experiment. Labels are generated by deterministic oracles over real Python
repositories; no teacher model or human judge labels the data.

## Intended Use

Use this checkpoint inside the repository's verified subroutine harness, which
renders the task-specific prompt, parses strict JSON, permits one localized
schema-feedback retry, applies deterministic guards, and falls back to rules
where appropriate. This is not a general coding assistant or chat model.

## Evaluation

Evaluation uses up to 250 examples from HTTPX and Jinja2, both held out
entirely from training. Decoding is greedy.

| Metric | Result |
|---|---:|
| Success after one schema retry | 64.8% |
| First-pass success | 64.8% |
| First-pass schema validity | 100.0% |
| Base instruct success after retry | 0.0% for the base instruct model |
| Rules-only success | 23.7% |

Experiment verdict for this subroutine: **unsolved (best 0.88 @ qwen2.5-0.5b)**.

## Training

- Training examples: 2000
- Epochs: 3.0
- Learning rate: 2e-05
- Effective batch configuration: 32 per device x
  1 gradient accumulation
- Maximum sequence length: 2048
- Seed: 0
- Final training loss: 0.529164
- Reproduction hardware: one NVIDIA A100 80GB PCIe
- Source revision:
  [`d0fd7bf`](https://github.com/IshaanAyaan/slm-agents/commit/d0fd7bff420c2f2f0446599ca2b169cc4f03b06a)

The dataset was generated from pinned Flask, Click, and Rich repositories for
training/validation. HTTPX and Jinja2 were reserved for testing.

## Limitations

The checkpoint is specialized to one closed JSON schema and should not be
expected to retain broad instruction-following ability. The experiment mixes
two base-model families across its size sweep. Some subroutines are better
served by deterministic rules; consult the verdict above before deployment.

## License

Apache-2.0, following the base model. Experiment code is MIT licensed.