130 lines
4.5 KiB
Markdown
130 lines
4.5 KiB
Markdown
# Kimi-Vendor-Verifier (Param Compliance)
|
||
|
||
|
||
## Overview
|
||
|
||
Kimi-Vendor-Verifier is a pre-flight compliance check for Kimi K2 / K2-Thinking deployments. It sends synthetic probe requests to verify that the vendor API correctly **rejects** non-default values of immutable decoding parameters (``temperature``, ``top_p``, ``presence_penalty``, ``frequency_penalty``, ``n``) and **accepts** their defaults. A vendor that silently accepts wrong values risks producing degraded model output that does not match official Moonshot AI behavior. Adapted from [Kimi-Vendor-Verifier/verify_params.py](https://github.com/MoonshotAI/Kimi-Vendor-Verifier/blob/main/verify_params.py).
|
||
|
||
## Task Description
|
||
|
||
- **Task Type**: API parameter-compliance probing (deployment health check)
|
||
- **Input**: A minimal chat message plus a single test parameter and thinking-mode `extra_body`
|
||
- **Output**: Whether the vendor accepted (HTTP 200) or rejected (HTTP 400) the request
|
||
- **Dataset**: Fully synthetic — no external dataset is downloaded; probes are generated in code from the K2 spec
|
||
|
||
## Key Features
|
||
|
||
- Synthetic probe set: one ``no_param`` sanity probe + 5 default-value (accept) probes + 5 wrong-value (reject) probes per (subset × thinking) combination
|
||
- Three subsets covering all common Kimi deployment shapes:
|
||
- ``kimi`` — official Moonshot SaaS API (``extra_body = {"thinking": {"type": ...}}``); thinking on/off
|
||
- ``opensource`` — vLLM / SGLang / KTransformers chat-template hook (``extra_body = {"chat_template_kwargs": {"thinking": ...}}``); thinking on/off
|
||
- ``none`` — non-hybrid model; no thinking parameter sent
|
||
- HTTP 400 responses are treated as the success signal when a reject was expected
|
||
- Single small request per probe; total cost is negligible compared to a full benchmark
|
||
|
||
## Evaluation Notes
|
||
|
||
- Default configuration uses **0-shot** synthetic probes
|
||
- Metrics: **param_immutable_reject_rate**, **param_default_accept_rate**, **inference_error_rate**
|
||
- Only HTTP 400 (``BadRequestError``) counts as a real parameter rejection; transport errors (5xx / timeout / 429) are excluded from the reject/accept denominators and surfaced via ``inference_error_rate`` so a flaky vendor doesn't get a free pass
|
||
- A correctly-deployed Kimi K2 vendor should report both rate metrics at **1.0** with ``inference_error_rate = 0``; anything less indicates a parameter-enforcement gap or transport instability
|
||
- For non-Kimi models, expect ``param_immutable_reject_rate = 0`` (no K2 spec to enforce) and ``param_default_accept_rate = 1.0`` (sensible defaults accepted)
|
||
- Select subset via ``dataset_args={'kimi_verifier': {'subset_list': ['kimi']}}`` (or ``opensource`` / ``none``)
|
||
|
||
|
||
## Properties
|
||
|
||
| Property | Value |
|
||
|----------|-------|
|
||
| **Benchmark Name** | `kimi_verifier` |
|
||
| **Dataset ID** | `kimi_verifier` |
|
||
| **Paper** | N/A |
|
||
| **Tags** | `Agent`, `FunctionCalling` |
|
||
| **Metrics** | `param_immutable_reject_rate`, `param_default_accept_rate`, `inference_error_rate` |
|
||
| **Default Shots** | 0-shot |
|
||
| **Evaluation Split** | `test` |
|
||
|
||
|
||
## Data Statistics
|
||
|
||
| Metric | Value |
|
||
|--------|-------|
|
||
| Total Samples | 55 |
|
||
| Prompt Length (Mean) | 26 chars |
|
||
| Prompt Length (Min/Max) | 26 / 26 chars |
|
||
|
||
**Per-Subset Statistics:**
|
||
|
||
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|
||
|--------|---------|-------------|------------|------------|
|
||
| `kimi` | 22 | 26 | 26 | 26 |
|
||
| `opensource` | 22 | 26 | 26 | 26 |
|
||
| `none` | 11 | 26 | 26 | 26 |
|
||
|
||
## Sample Example
|
||
|
||
**Subset**: `kimi`
|
||
|
||
```json
|
||
{
|
||
"input": [
|
||
{
|
||
"id": "03c069db",
|
||
"content": "Say 'OK' and nothing else."
|
||
}
|
||
],
|
||
"target": "",
|
||
"id": 0,
|
||
"group_id": 0,
|
||
"subset_key": "kimi",
|
||
"metadata": {
|
||
"think_mode": "kimi",
|
||
"thinking": false,
|
||
"param_name": null,
|
||
"test_value": null,
|
||
"expected_reject": false
|
||
}
|
||
}
|
||
```
|
||
|
||
## Prompt Template
|
||
|
||
*No prompt template defined.*
|
||
|
||
## Usage
|
||
|
||
### Using CLI
|
||
|
||
```bash
|
||
evalscope eval \
|
||
--model YOUR_MODEL \
|
||
--api-url OPENAI_API_COMPAT_URL \
|
||
--api-key EMPTY_TOKEN \
|
||
--datasets kimi_verifier \
|
||
--limit 10 # Remove this line for formal evaluation
|
||
```
|
||
|
||
### Using Python
|
||
|
||
```python
|
||
from evalscope import run_task
|
||
from evalscope.config import TaskConfig
|
||
|
||
task_cfg = TaskConfig(
|
||
model='YOUR_MODEL',
|
||
api_url='OPENAI_API_COMPAT_URL',
|
||
api_key='EMPTY_TOKEN',
|
||
datasets=['kimi_verifier'],
|
||
dataset_args={
|
||
'kimi_verifier': {
|
||
# subset_list: ['kimi', 'opensource', 'none'] # optional, evaluate specific subsets
|
||
}
|
||
},
|
||
limit=10, # Remove this line for formal evaluation
|
||
)
|
||
|
||
run_task(task_cfg=task_cfg)
|
||
```
|
||
|
||
|