---
license: apache-2.0
tags:
  - baseweight-benchmark
  - benchmark
  - predictions
---

# Baseweight Benchmark — Raw Predictions

Every model generation produced during evaluation for the
[Baseweight Benchmark](https://baseweight.co/benchmark), a reproducible
comparison of fine-tuned open models against frontier APIs on focused vertical
tasks.

## What's here

One JSONL file per model × task × condition × seed. Each line is one
prediction:

| Field | Description |
|---|---|
| `id` | Row identifier from the prepared test split |
| `model` | Model short name (`qwen3-8b`, `gpt-5.4-mini`) |
| `condition` | `lora`, `zero-shot`, or `5-shot` |
| `eval_seed` | Seed used for this eval run (0, 1, or 2) |
| `prompt_sha` | SHA-256 prefix of the prompt template used |
| `input` | The text input sent to the model |
| `output` | The raw model output |
| `ground_truth` | Gold label or answer |
| `input_tokens` | Prompt token count |
| `output_tokens` | Completion token count |
| `avg_logprob` | Mean log-probability of output tokens |
| `timestamp` | UTC time of the generation |

## File layout

```
predictions/
  local/
    qwen3-8b/
      banking77/   lora.jsonl  lora_seed1.jsonl  lora_seed2.jsonl
                   zero-shot.jsonl  zero-shot_seed1.jsonl  zero-shot_seed2.jsonl
                   5-shot.jsonl  5-shot_seed1.jsonl  5-shot_seed2.jsonl
      cuad/        lora.jsonl  lora_seed1.jsonl  lora_seed2.jsonl
                   zero-shot.jsonl  zero-shot_seed1.jsonl  zero-shot_seed2.jsonl
  api/
    gpt-5.4-mini/
      banking77/   zero-shot.jsonl  zero-shot_seed1.jsonl  zero-shot_seed2.jsonl
                   5-shot.jsonl  5-shot_seed1.jsonl  5-shot_seed2.jsonl
      cuad/        zero-shot.jsonl  zero-shot_seed1.jsonl  zero-shot_seed2.jsonl
```

Seeds 1 and 2 are independent re-evaluations with shuffled test order.
Seed 0 is the base run (file without seed suffix). Reported metrics are the
mean across all three seeds; 95% CIs are bootstrap intervals over the seeds.

## Verifying the numbers

Download any JSONL, recompute the metric against `ground_truth`, and compare
to the value in `results.json` on the benchmark site. The `prompt_sha` field
lets you confirm the exact prompt template used.

## Links

- Benchmark site: [baseweight.co/benchmark](https://baseweight.co/benchmark)
- Pipeline code: [github.com/baseweight-ai/benchmark](https://github.com/baseweight-ai/benchmark)
- Prepared data splits: [baseweight-ai/baseweight-benchmark-data](https://huggingface.co/datasets/baseweight-ai/baseweight-benchmark-data)
- Fine-tuned adapters: [baseweight-ai/baseweight-benchmark-adapters](https://huggingface.co/collections/baseweight-ai/baseweight-benchmark-adapters-6a1613a5bee5213ab5e44936)