coli bench: self-contained (venv python + auto-fetch datasets); README calls for a quality run on faster hardware

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

README.md

@@ -114,6 +114,19 @@ PIN=stats.txt PIN_GB=20 ./coli chat        # scale PIN_GB to your free RAM
 
 These are estimates, not measurements — if you run colibrì on serious hardware, **please open an issue with your numbers**: real datapoints from better machines are exactly what this project needs next.
 
+## Quality benchmark — help wanted
+
+We have never measured how much the int4 quantization costs in accuracy — the harness is built and wired, but scoring is one forward per answer option, and on the dev box's ~1 GB/s disk a full run takes the better part of a day. **This is the single most valuable thing a faster machine can contribute.** The code is here and ready; one command runs it end to end (it auto-downloads the datasets on first use):
+
+```bash
+cd c
+./coli bench                                   # hellaswag, arc_challenge, mmlu — 40 questions each
+./coli bench hellaswag --limit 200             # one task, more questions
+./coli bench mmlu arc_challenge --ram 100      # pick tasks, set a RAM budget
+```
+
+It prints per-task accuracy (log-likelihood scoring, EleutherAI-harness style). Published full-precision GLM-5.2 scores on these tasks sit around 85–95%; if our int4 container lands within a few points, the quantization is validated — if it doesn't, we know to invest in mixed / grouped-scale quantization. **If you have the hardware to run this, please open an issue with the numbers** — it's the measurement the project is missing.
+
 ## Supporting the project
 
 colibrì is a one-person project, written and tested entirely on a 12-core laptop with 25 GB of RAM — the numbers above are the ceiling of what I can measure at home. If this project is useful or interesting to you and you'd like to support its development (better test hardware translates *directly* into a faster engine for everyone: real NVMe scaling data, bigger pinned caches, int2/int3 quality sweeps on real benchmarks), you can:

c/coli

@@ -288,13 +288,23 @@ def cmd_chat(a):
 def cmd_bench(a):
     need_model(a.model)
     banner("bench")
-    cmd=[sys.executable, os.path.join(HERE,"eval_glm.py"), "--snap",a.model,
-         "--tasks", ",".join(a.tasks) if a.tasks else "hellaswag,arc_challenge,mmlu",
-         "--limit", str(a.limit), "--data", a.data]
+    # python con `tokenizers`: l'ambiente del progetto se c'e', altrimenti quello corrente
+    venv_py=os.path.join(HERE,"mio_env","bin","python3")
+    py = venv_py if os.path.exists(venv_py) else sys.executable
+    tasks = ",".join(a.tasks) if a.tasks else "hellaswag,arc_challenge,mmlu"
+    # dataset mancanti -> li scarica una volta (fetch_benchmarks.py li mette in --data come JSONL)
+    missing=[t for t in tasks.split(",") if not os.path.exists(os.path.join(a.data,f"{t}.jsonl"))]
+    if missing:
+        print(f"  {C.dim}scarico i dataset mancanti: {', '.join(missing)}{C.r}")
+        subprocess.call([py, os.path.join(HERE,"fetch_benchmarks.py"),
+                         "--out", a.data, "--tasks", ",".join(missing), "--limit", str(max(a.limit,200))])
+    cmd=[py, os.path.join(HERE,"eval_glm.py"), "--snap",a.model,
+         "--tasks", tasks, "--limit", str(a.limit), "--data", a.data]
     if a.ram: cmd+=["--ram",str(a.ram)]
     e=dict(os.environ)
     if a.topp: e["TOPP"]=str(a.topp)
     if a.topk: e["TOPK"]=str(a.topk)
+    print(f"  {C.dim}decode disk-bound: su hardware lento questo richiede ORE. Alza --limit su macchine veloci.{C.r}\n")
     sys.exit(subprocess.call(cmd, env=e))
 
 def cmd_convert(a):