Public-source quant fidelity eval: 140 on-policy + 100 forced prompts, manifest, builder

README.md

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+---
+pretty_name: Quant fidelity eval (public sources)
+tags:
+- quantization
+- evaluation
+- kl-divergence
+- agentic
+- tool-use
+configs:
+- config_name: default
+  data_files:
+  - split: on_policy
+    path: public_on_prompts.jsonl
+  - split: forced
+    path: public_off_prompts.jsonl
+---
+
+# Quant fidelity eval (public sources)
+
+This dataset holds prompts for measuring how closely a quantized model reproduces its full-precision original. All 240
+prompts come from seven public datasets.
+
+The measurement is per-token KL divergence between the original model and the quantized one. It is computed on two
+kinds of text:
+
+- **`on_policy`** (140 prompts): the original model continues each prompt greedily, here for 512 tokens. Each quant is
+  then scored on that same text.
+- **`forced`** (100 prompts): the dataset's own assistant turn is fed to both models (teacher forcing), here up to 768
+  tokens. The turns include reasoning, code edits and tool calls.
+
+Prompts are stored as chat messages and are rendered with the evaluated model's own chat template when the reference
+is frozen. A prompt with `forced_turn: true` ends in the assistant turn to force; every other prompt ends where the
+assistant should reply.
+
+## Sources
+
+| Source | Dataset | Licence (per its card) | On-policy | Forced |
+| --- | --- | --- | ---: | ---: |
+| ultrachat | [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), `test_sft` | MIT | 20 | 15 |
+| openhermes | [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | not stated on card | 20 | 15 |
+| tulu3 | [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), WildChat rows excluded | ODC-BY | 25 | 15 |
+| openorca | [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | MIT | 15 | 0 |
+| swe_agent | [nebius/SWE-agent-trajectories](https://huggingface.co/datasets/nebius/SWE-agent-trajectories) | CC-BY-4.0 | 20 | 20 |
+| openhands | [SWE-Gym/OpenHands-SFT-Trajectories](https://huggingface.co/datasets/SWE-Gym/OpenHands-SFT-Trajectories), `train.success.oss` | MIT | 20 | 20 |
+| glaive_fc | [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | Apache-2.0 | 20 | 15 |
+
+The prompt text belongs to these datasets and is used here for evaluation only. Their licences and attribution terms
+apply.
+
+## Selection
+
+Selection is deterministic, so anyone can rebuild the set with `build_public.py`.
+
+- **Pinned revisions.** Each dataset is read at the revision recorded in `public_manifest.json`.
+- **Order.** Rows are read in a seeded streaming shuffle (seed 0, buffer 5000). OpenHands is read in file order.
+- **Quotas.** Rows fill the on-policy quota first, then the forced quota. Each row is used at most once.
+- **Cut point.** The turn to cut at, or to force, is `sha256(source, row id, set) mod (number of eligible turns)`.
+- **Eligible turns.** At most 24,000 characters of context may come before the cut. A forced turn must be at least
+  800 characters for chat sources, 400 for the agent trajectories and 150 for function calling.
+- **Format conversions.**
+  - SWE-agent: `ai` turns become assistant messages.
+  - Glaive: `FUNCTION RESPONSE` turns become user messages prefixed "Function response:".
+
+`public_manifest.json` lists, for every prompt, the dataset, revision, split, row id and turn index.
+
+## Files
+
+| File | Contents |
+| --- | --- |
+| `public_on_prompts.jsonl`, `public_off_prompts.jsonl` | One prompt per line: `id`, `category`, `messages` and, for forced prompts, `forced_turn` |
+| `public_on_prompts.json`, `public_off_prompts.json` | The same prompts in the prompt-file format the autoadd v2 reference freezer reads |
+| `public_manifest.json` | How each prompt was selected |
+| `build_public.py` | The builder |
+
+## Usage
+
+1. **Freeze the references.** Freeze the original model's outputs once, for example with autoadd v2:
+
+   ```
+   autoadd_ref.py --hf-model <bf16 checkpoint> --out-dir <dir> \
+     --set public_on=public_on_prompts.json:512:floor=0 \
+     --set public_off=public_off_prompts.json:768:floor=0
+   ```
+
+2. **Score each quant.** Score with autoquant v2:
+
+   ```
+   python -m autoquant.eval.measure measure --engine gguf --model <quant.gguf> \
+     --goldens <dir>/reference_public_on.json --logprob-dir <dir>/ref_logprobs_public_on \
+     --off-goldens <dir>/reference_public_off.json --off-logprob-dir <dir>/ref_logprobs_public_off --out result.json
+   ```
+
+3. **Compare.** Report the pooled mean, p50, p99 and max KL. Bootstrap over prompts, not tokens: tokens within one
+   prompt are strongly correlated.
+
+## Caveats
+
+- **Frozen references are not included.** References depend on the model and the hardware, and their log-prob rows are
+  tens of GB. Freeze your own.
+- **The base model may have seen these datasets in training.** That does not affect a comparison between quants of the
+  same model, because KL measures only how faithfully each quant reproduces the original.
+- **UltraChat KL is unusually high** for every quant we have measured, about 10× the other sources. It is still being
+  investigated, so report results both with and without it.

build_public.py

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+"""Public goldens prompt sets for quant fidelity (no internal data). Deterministic: pinned revisions, seeded
+streaming shuffle, hash-chosen cut points. Writes autoadd-format prompt files + a publishable manifest."""
+import hashlib, json, re, sys
+from datasets import load_dataset
+OUT = sys.argv[1]; SEED = 0; MAX_PREFIX = 24000
+H = lambda s: int(hashlib.sha256(s.encode()).hexdigest()[:8], 16)
+def mchars(m): return len(m.get("content") or "")
+def norm_ultrachat(r): return r["prompt_id"], [{"role": m["role"], "content": m["content"]} for m in r["messages"]]
+def norm_hermes(r):
+    conv = r["conversations"]
+    if isinstance(conv, str):
+        try: conv = json.loads(conv)
+        except ValueError: return None
+    if not isinstance(conv, list): return None
+    try: conv = [json.loads(c) if isinstance(c, str) else c for c in conv]
+    except ValueError: return None
+    if not all(isinstance(c, dict) for c in conv): return None
+    r = dict(r, conversations=conv)
+    rid = r.get("id") or r.get("hash") or (str(r["idx"]) if r.get("idx") is not None else None) or hashlib.sha256(json.dumps(r["conversations"]).encode()).hexdigest()[:16]
+    role = {"system": "system", "human": "user", "gpt": "assistant"}
+    return str(rid), [{"role": role[c["from"]], "content": c["value"]} for c in r["conversations"] if c["from"] in role]
+def norm_tulu(r):
+    if "wildchat" in (r.get("source") or "").lower(): return None
+    return r["id"], [{"role": m["role"], "content": m["content"]} for m in r["messages"]]
+def norm_orca(r):
+    msgs = ([{"role": "system", "content": r["system_prompt"]}] if r["system_prompt"] else [])
+    return r["id"], msgs + [{"role": "user", "content": r["question"]}, {"role": "assistant", "content": r["response"]}]
+def norm_sweagent(r):
+    role = {"system": "system", "user": "user", "ai": "assistant"}; out = []
+    for m in r["trajectory"]:
+        c = m.get("system_prompt") if m["role"] == "system" else m.get("text")
+        if m["role"] in role and c: out.append({"role": role[m["role"]], "content": c})
+    return f"{r['instance_id']}|{r['model_name']}", out
+def norm_openhands(r, i=[0]):
+    i[0] += 1; msgs = [{"role": m["role"], "content": m["content"]} for m in r["messages"] if m["role"] in ("system", "user", "assistant")]
+    return hashlib.sha256(json.dumps(r["messages"][:3]).encode()).hexdigest()[:16], msgs
+def norm_glaive(r):
+    parts = re.split(r"(USER:|ASSISTANT:|FUNCTION RESPONSE:)", r["chat"]); msgs = []
+    sysm = re.sub(r"^SYSTEM:\s*", "", r["system"]).strip()
+    if sysm: msgs.append({"role": "system", "content": sysm})
+    for tag, body in zip(parts[1::2], parts[2::2]):
+        body = body.replace("<|endoftext|>", "").strip()
+        if not body: continue
+        if tag == "USER:": msgs.append({"role": "user", "content": body})
+        elif tag == "ASSISTANT:": msgs.append({"role": "assistant", "content": body})
+        else: msgs.append({"role": "user", "content": "Function response: " + body})
+    return hashlib.sha256((r["system"] + r["chat"]).encode()).hexdigest()[:16], msgs
+SOURCES = [  # name, repo, revision, split, normaliser, greedy quota, forced quota, min forced chars, shuffle
+ ("ultrachat", "HuggingFaceH4/ultrachat_200k", "8049631c40", "test_sft", norm_ultrachat, 20, 15, 800, True),
+ ("openhermes", "teknium/OpenHermes-2.5", "b820378210", "train", norm_hermes, 20, 15, 800, True),
+ ("tulu3", "allenai/tulu-3-sft-mixture", "b14afda60f", "train", norm_tulu, 25, 15, 800, True),
+ ("openorca", "Open-Orca/OpenOrca", "e9c87b4abb", "train", norm_orca, 15, 0, 800, True),
+ ("swe_agent", "nebius/SWE-agent-trajectories", "68195a1450", "train", norm_sweagent, 20, 20, 400, True),
+ ("openhands", "SWE-Gym/OpenHands-SFT-Trajectories", "4aaa5a4a4b", "train.success.oss", norm_openhands, 20, 20, 400, False),
+ ("glaive_fc", "glaiveai/glaive-function-calling-v2", "e7f4b64560", "train", norm_glaive, 20, 15, 150, True),
+]
+on, off, manifest = [], [], []
+for name, repo, rev, split, norm, qg, qf, minf, shuf in SOURCES:
+    from huggingface_hub import HfApi
+    full_rev = HfApi().dataset_info(repo).sha; assert full_rev.startswith(rev), (repo, full_rev)
+    ds = load_dataset(repo, split=split, streaming=True, revision=full_rev)
+    if shuf: ds = ds.shuffle(seed=SEED, buffer_size=5000)
+    ng = nf = 0; seen = 0
+    for r in ds:
+        if ng >= qg and nf >= qf: break
+        seen += 1
+        x = norm(r)
+        if not x: continue
+        rid, msgs = x
+        pre = [0]
+        for m in msgs: pre.append(pre[-1] + mchars(m))
+        def cands(forced):
+            return [k for k, m in enumerate(msgs) if m["role"] == "assistant" and k >= 1 and pre[k] <= MAX_PREFIX
+                    and any(y["role"] == "user" for y in msgs[:k]) and (not forced or mchars(m) >= minf)]
+        if ng < qg and cands(False):
+            c = cands(False); k = c[H(f"{name}\0{rid}\0on") % len(c)]
+            on.append({"id": f"{name}-{ng:02d}", "category": name, "messages": msgs[:k]})
+            manifest.append({"set": "public_on", "id": f"{name}-{ng:02d}", "dataset": repo, "revision": full_rev, "split": split, "row_id": rid, "cut_before_message": k}); ng += 1
+        elif nf < qf and cands(True):
+            c = cands(True); k = c[H(f"{name}\0{rid}\0off") % len(c)]
+            off.append({"id": f"{name}-{nf:02d}", "category": name, "messages": msgs[:k + 1], "forced_turn": True})
+            manifest.append({"set": "public_off", "id": f"{name}-{nf:02d}", "dataset": repo, "revision": full_rev, "split": split, "row_id": rid, "forced_message": k}); nf += 1
+    print(f"{name}: greedy {ng}/{qg} forced {nf}/{qf} (rows read {seen})", flush=True)
+for fname, ents, what in (("public_on_prompts.json", on, "greedy BF16 continuation, 512 tokens"),
+                          ("public_off_prompts.json", off, "forced dataset assistant turn, up to 768 tokens")):
+    json.dump({"comment": f"public quant-fidelity set: {what}. Sources and selection in public_manifest.json.",
+               "key_tokenizer": True, "prompts": ents}, open(f"{OUT}/{fname}", "w"), ensure_ascii=False, indent=1, sort_keys=True)
+json.dump({"seed": SEED, "max_prefix_chars": MAX_PREFIX, "selection": "seeded streaming shuffle (buffer 5000) except OpenHands; "
+           "rows in order fill the greedy quota first, then forced; cut point = sha256(source, row id, set) mod candidates; Tulu WildChat rows excluded",
+           "prompts": manifest}, open(f"{OUT}/public_manifest.json", "w"), indent=1)
+print("on", len(on), "off", len(off))

public_manifest.json

@@ -0,0 +1,2167 @@
+{
+ "seed": 0,
+ "max_prefix_chars": 24000,
+ "selection": "seeded streaming shuffle (buffer 5000) except OpenHands; rows in order fill the greedy quota first, then forced; cut point = sha256(source, row id, set) mod candidates; Tulu WildChat rows excluded",
+ "prompts": [
+  {
+   "set": "public_on",
+   "id": "ultrachat-00",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "2ddbb503ac33ef9c7fa9e58d1415a0777bcb7d83d842006f56aeffe0de2c76c1",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-01",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "ab529baa3cef212c0a3067ad8f7950fe9a27483356505ca112fae420ff65b16f",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-02",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "86a902b57e06fa9d01d991ffa28b4a5a8625fb9ada712ad7f03a5991e3f506b3",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-03",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "d33ce4ff97174d4dec065ac4bff7f1a8f9fad11c7a79d1d21e8b1a2eff86a92a",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-04",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "ba2a844984336b4243556f3650ff0f17bae7a2b7dc33c68e2bbe437c333ae63b",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-05",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "92dd55a0d722df422020e95498fb1491ddce8abf559531654b4c2eed09f5cf00",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-06",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "24d4950409d7e38752bb979209178a58d50d1bb7985070476486a8e28478ef08",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-07",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "572ef394f8b2282872128605b39115237080ae4b52da8c235a077e0622ee8f92",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-08",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "013b94a046ac7aed0113914a12587821a8469255dffae39d12fe507fd3e1b799",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-09",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "7aa277f15ce3dd2a87f2c9ec9448a99040ad6f25ec3f640dad158b39b07d3140",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-10",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "15f6202f7dd2a1af21a3749547a03fb93ff319f339cd38cd3bea6cae7c0819dd",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-11",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "a31b022bc28354a3d92606e2120289ceb6301fa17b4abb9088ce3c6e532539c2",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-12",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "4a0bd58b905e01de721957ea279574a0b9c7581bdd22ef02efd8a0babe33d1dc",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-13",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "f288dba061fd2210fafd91d170d966401a9a86f49f54c40823ee09c2c231fcf5",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-14",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "fb25aa4adbcd3f5f8716453f706cb07a0ce5a46cbacaf75ee9be5caeb977ab34",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-15",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "f39c73a1696100ba920889ac2cffc27aacd1450930136307ba6dbdfb6cd4151f",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-16",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "3740219b127f283d7478822a68341e35d740a4efbe29087bdfc075f18cc4d2f2",
+   "cut_before_message": 5
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-17",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "f15d11f5ba0f72233d47a138f7e75f9e1df8c1c3866982e70ec0181d6382de3f",
+   "cut_before_message": 3
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-18",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "04337d457a96640954fbc5f90f16fef3e8e8e79b60be0664f724ce802f393347",
+   "cut_before_message": 1
+  },
+  {
+   "set": "public_on",
+   "id": "ultrachat-19",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "ced982fb36a2926296cb748d11b55a17f5493128b5a650f1dbb0983c0ea91508",
+   "cut_before_message": 5
+  },
+  {
+   "set": "public_off",
+   "id": "ultrachat-00",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "aeca617d931fb92b7b85a632550d277dbe9a26efdce318784c000aa30f85c61f",
+   "forced_message": 5
+  },
+  {
+   "set": "public_off",
+   "id": "ultrachat-01",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
+   "row_id": "6102db44126803611202d19a5c5c96225a5d28fa3f6d33d4cb4647276d4c742b",
+   "forced_message": 7
+  },
+  {
+   "set": "public_off",
+   "id": "ultrachat-02",
+   "dataset": "HuggingFaceH4/ultrachat_200k",
+   "revision": "8049631c405ae6576f93f445c6b8166f76f5505a",
+   "split": "test_sft",
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