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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
language:
- en
pipeline_tag: text-generation
tags:
- router
- prompt-router
- structured-output
- json
- code
- qwen2.5-coder
- gguf
---
# DevRouter-1.5B
**A tiny, fast router that turns a raw developer prompt into a single structured JSON decision.**
DevRouter-1.5B reads a raw coding prompt and returns one JSON object that (1) rewrites the prompt
into a cleaner version, (2) classifies its **intent**, **complexity**, and a suggested **route**
(which model tier to send it to), and (3) flags **missing context** the developer should have
included. It is meant to sit *in front of* your expensive models and make a cheap, deterministic
triage call in ~1–3 seconds on a single consumer GPU.
It is a fine-tune of **Qwen2.5-Coder-1.5B-Instruct** (Apache 2.0), distilled on a dataset of
developer prompts labelled by a stronger teacher model.
## Output schema
Every response is a single JSON object with exactly these five fields:
| field | type | values |
|---|---|---|
| `rewrite` | string | a clearer version of the prompt, preserving the original intent |
| `intent` | enum | `debug` · `refactor` · `feature` · `explain` · `documentation` · `boilerplate` · `architecture` · `review` · `optimize` · `other` |
| `complexity` | enum | `low` · `medium` · `high` |
| `route` | enum | `small_local` · `medium_api` · `large_api` |
| `missing` | array of strings | context the prompt should have included (empty if nothing) |
### Example
**Input (user message):**
> My Flask app 500s on POST /upload with RequestEntityTooLarge, how do I fix it?
**Output:**
```json
{
"rewrite": "I'm encountering a 500 Internal Server Error on my Flask app when handling POST /upload. The error is 'RequestEntityTooLarge'. How can I resolve this issue?",
"intent": "debug",
"complexity": "low",
"route": "small_local",
"missing": ["Flask version", "Python version"]
}
```
## Quick start
The router system prompt is **baked into the model's chat template**, so you do not need to supply a
system prompt — just send the raw developer prompt as the user message.
### Ollama
```bash
# from the -GGUF repo (Modelfile + DevRouter-1.5B-Q8_0.gguf)
ollama create devrouter -f Modelfile
ollama run devrouter "refactor this giant function into smaller ones"
```
### llama.cpp
```bash
llama-server -m DevRouter-1.5B-Q8_0.gguf -c 8192 -ngl 99 --parallel 1
# then POST to /v1/chat/completions with just a user message, temperature 0
```
### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("aipster/DevRouter-1.5B")
model = AutoModelForCausalLM.from_pretrained("aipster/DevRouter-1.5B")
msgs = [{"role": "user", "content": "write a FastAPI POST /items endpoint with a Pydantic model"}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=1408, do_sample=False)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
```
Use **greedy decoding (`temperature=0`)** for stable, parseable JSON.
## Evaluation
Evaluated on a held-out, intent-stratified validation split (`val`, in-distribution) and an
out-of-distribution split (`holdout`, no GitHub-issue sources). Metrics: rate of valid JSON
(strict schema parse) and accuracy of `intent` / `route` / `complexity` against the teacher labels.
| metric | fp16 (val / holdout) | Q8_0 GGUF (val / holdout) |
|---|---|---|
| JSON validity | 0.973 / 0.955 | 0.965 / 0.946 |
| intent accuracy | 0.708 / 0.613 | 0.665 / 0.586 |
| route accuracy | 0.739 / 0.604 | 0.719 / 0.631 |
| complexity accuracy | 0.719 / 0.685 | 0.708 / 0.676 |
Per-intent accuracy (Q8_0, val):
| intent | acc | intent | acc |
|---|---|---|---|
| debug | 0.82 | architecture | 0.72 |
| refactor | 0.72 | documentation | 0.56 |
| explain | 0.73 | boilerplate | 0.64 |
| feature | 0.58 | review | 0.43 |
| optimize | 0.50 | | |
## Performance
Single RTX 3090, Q8_0 GGUF via llama.cpp (single stream):
- **Generation: ~280 tokens/s** (~3.6 ms/token, constant across output lengths)
- **Prompt eval (prefill): ~10,000–13,000 tokens/s**
- **Latency per routing call: ~1–3 s** (up to ~5 s for the longest outputs)
Throughput scales further with batching/concurrency.
## Limitations
- **No PII detection.** An earlier schema included a PII flag; it was removed from v1.1 because the
training data had too few PII-positive examples (~2.4%) to learn it reliably. Do **not** use this
model for privacy/safety filtering. (Planned for a future data-focused release.)
- **Weaker on rare intents.** `review` and `documentation` are under-represented and noisier in the
training data, and accuracy on them is lower.
- **Source bias / OOD gap.** Training prompts skew heavily toward GitHub-issue-style text, so intent
accuracy drops ~10 points on out-of-distribution prompts. Treat `route`/`complexity` as advisory.
- **Quantization sensitivity.** This is a small model doing strict structured output. **Q6_K and
below break the JSON** (validity collapses); ship **Q8_0** or **F16** only. Always validate a
quant before relying on it.
## Training
- **Base:** Qwen2.5-Coder-1.5B-Instruct (Apache 2.0)
- **Method:** QLoRA (4-bit), rank 16, 2 epochs, effective batch 16, `train_on_responses_only`
- **Data:** ~2.6k developer prompts, each labelled by a stronger teacher model into the 5-field
schema, filtered by a judge model and capped per intent. Distillation sources permit training and
open release of derived weights.
## Intended use
Pre-routing / triage of developer prompts in an LLM application: rewriting, intent/complexity
classification, and model-tier selection. **Not** intended for safety filtering, PII detection, or
as a general assistant.
## License
Apache 2.0, inherited from the base model. You are free to use, modify, and redistribute, including
commercially.

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are a coding prompt router. You receive raw developer prompts and return a single JSON object with these fields:\n\n- rewrite (string): Clearer version of the prompt, preserving original intent. Do not invent requirements not present in the source.\n- intent (enum): debug | refactor | feature | explain | documentation | boilerplate | architecture | review | optimize | other\n- complexity (enum): low | medium | high\n- route (enum): small_local | medium_api | large_api\n- missing (array of strings): Context the developer should have included but didn\'t. Empty array if nothing is missing.\n\nReturn ONLY valid JSON. No prose outside the JSON.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are a coding prompt router. You receive raw developer prompts and return a single JSON object with these fields:\n\n- rewrite (string): Clearer version of the prompt, preserving original intent. Do not invent requirements not present in the source.\n- intent (enum): debug | refactor | feature | explain | documentation | boilerplate | architecture | review | optimize | other\n- complexity (enum): low | medium | high\n- route (enum): small_local | medium_api | large_api\n- missing (array of strings): Context the developer should have included but didn\'t. Empty array if nothing is missing.\n\nReturn ONLY valid JSON. No prose outside the JSON.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a coding prompt router. You receive raw developer prompts and return a single JSON object with these fields:\\n\\n- rewrite (string): Clearer version of the prompt, preserving original intent. Do not invent requirements not present in the source.\\n- intent (enum): debug | refactor | feature | explain | documentation | boilerplate | architecture | review | optimize | other\\n- complexity (enum): low | medium | high\\n- route (enum): small_local | medium_api | large_api\\n- missing (array of strings): Context the developer should have included but didn\\'t. Empty array if nothing is missing.\\n\\nReturn ONLY valid JSON. No prose outside the JSON.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a coding prompt router. You receive raw developer prompts and return a single JSON object with these fields:\\n\\n- rewrite (string): Clearer version of the prompt, preserving original intent. Do not invent requirements not present in the source.\\n- intent (enum): debug | refactor | feature | explain | documentation | boilerplate | architecture | review | optimize | other\\n- complexity (enum): low | medium | high\\n- route (enum): small_local | medium_api | large_api\\n- missing (array of strings): Context the developer should have included but didn\\'t. Empty array if nothing is missing.\\n\\nReturn ONLY valid JSON. No prose outside the JSON.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}

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