README.md

---
license: apache-2.0
---

[Run `fix-json` on
Synthetic!](https://synthetic.new/hf/syntheticlab/fix-json)
IMPORTANT: Make sure to set `temperature` to zero, either via the API or in the
settings UI on Synthetic (accessed via the gear icon).

`fix-json` is a small, fast model to fix broken JSON. While most LLM inference
providers support a "JSON mode" that enforces model responses are valid JSON,
many *don't* support strict JSON or schema adherence for function calls. While
coding LLMs are typically fairly good at generating JSON, at a certain level of
nesting they can make small mistakes like forgetting to triple-escape some
deeply nested control character.

When that happens, most agent frameworks force retries. Retries are bad for a
couple of reasons:

1. They're slow, and typically expensive, since you're using a fairly large
   model to do the retry
2. They often can confuse the model: models are trained reasonably well on the
   happy path where they generate working code and tool calls, but often suffer
   when attempting to self-correct.

`fix-json` is trained to fix those broken JSON strings, allowing coding agents
to typically skip (expensive, slow, model-confusing) retries, fairly robustly.
It's a Llama 3.1 8b LoRA, which means you can run it cheaply on inference
providers like [Synthetic](https://synthetic.new/hf/syntheticlab/diff-apply).
It was trained against editing any inner string contents, and attempts to only
make the minimal control character edits possible to fix the JSON string.

The model is generally fairly robust to prompts, but for best performance you
should use the original prompt it was trained on:

```

The following string may be broken JSON. Fix it if possible. Respond with JSON in the following
format, defined as TypeScript types:

// Success response:
type JsonFixSuccess = {
  success: true,

  // The parsed JSON
  fixed: any,
};

// Failure response:
type JsonFixFailure = {
  success: false,
};

If it's more-or-less JSON, fix it and respond with the success response. If it's not, respond with
the failure response. Here's the string:
```

You should append the broken JSON string to the end of the prompt. It'll
respond with either `{ "success": false }` if the string is too ambiguous or
doesn't appear to be JSON; otherwise, it'll generate `{ "success": true,
"fixed": ... }`, where `fixed` is the JSON.

Since the model responds with pure JSON, it's suitable for use with LLM
inference providers that allow JSON mode, making it even more robust at fixing
JSON.

## Training details

Trained on a single H100 NVL for 2.5 hours using Axolotl.