Download scripts/generate.py from fabsssss/ssao-space-instruct: direct link, hf CLI and curl.
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https://huggingface.co/datasets/fabsssss/ssao-space-instruct/resolve/main/scripts/generate.py
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hf download hf://datasets/fabsssss/ssao-space-instruct/scripts/generate.py
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curl -L -o generate.py https://huggingface.co/datasets/fabsssss/ssao-space-instruct/resolve/main/scripts/generate.py
2.19 kB
| #!/usr/bin/env python3 | |
| """ | |
| Generate held-out test completions from a local MLX model (baseline or fused | |
| adapter) and tag each record with its task family so spaceval can score it. | |
| Usage: | |
| python scripts/generate.py <model-path-or-hf-id> <out.jsonl> [--adapter PATH] [--limit N] | |
| """ | |
| import argparse | |
| import json | |
| import pathlib | |
| import re | |
| ROOT = pathlib.Path("/Users/fabio/projects/qwen-space-ft") | |
| def task_of(user: str, gold: str) -> str: | |
| u = user.lower() | |
| if "as ssao turtle" in u or "express this catalogue record" in u: | |
| return "turtle" | |
| if "matcher proposes" in u: | |
| return "align" | |
| if "classify the orbit regime" in u: | |
| return "regime" | |
| if u.startswith("which ssao class"): | |
| return "lookup" | |
| return "refusal" | |
| def main(): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("model") | |
| ap.add_argument("out") | |
| ap.add_argument("--adapter", default=None) | |
| ap.add_argument("--limit", type=int, default=0) | |
| ap.add_argument("--max-tokens", type=int, default=420) | |
| args = ap.parse_args() | |
| from mlx_lm import load, generate | |
| from mlx_lm.sample_utils import make_sampler | |
| kw = {} | |
| if args.adapter: | |
| kw["adapter_path"] = args.adapter | |
| model, tokenizer = load(args.model, **kw) | |
| sampler = make_sampler(temp=0.0) | |
| rows = [json.loads(l) for l in open(ROOT / "data" / "test.jsonl")] | |
| if args.limit: | |
| rows = rows[: args.limit] | |
| out = open(args.out, "w") | |
| for i, row in enumerate(rows, 1): | |
| msgs = row["messages"] | |
| gold = msgs[2]["content"] | |
| prompt = tokenizer.apply_chat_template( | |
| msgs[:2], add_generation_prompt=True, tokenize=False) | |
| text = generate(model, tokenizer, prompt=prompt, | |
| max_tokens=args.max_tokens, sampler=sampler, verbose=False) | |
| out.write(json.dumps({"task": task_of(msgs[1]["content"], gold), | |
| "user": msgs[1]["content"], "gold": gold, | |
| "output": text}) + "\n") | |
| out.flush() | |
| if i % 10 == 0: | |
| print(f" {i}/{len(rows)}", flush=True) | |
| out.close() | |
| print("wrote", args.out) | |
| if __name__ == "__main__": | |
| main() | |