Update files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+qwen3_4b_tiled64/tokenizer.json filter=lfs diff=lfs merge=lfs -text

eval/eval_model.sh

@@ -0,0 +1,53 @@
+#!/bin/bash
+# Combined perplexity + zero-shot eval for one checkpoint. Just point it at a model.
+#
+#   eval_model.sh <model_path_or_hf_id> [name] [gpu]
+#
+# Runs, on one GPU, sequentially (PPL via HF, then zero-shot via lm_eval+vllm):
+#   - WT2 + C4 perplexity
+#   - 7-task zero-shot (arc_easy, arc_challenge, hellaswag, winogrande, piqa,
+#     boolq, openbookqa)
+# then prints a combined table. Results go under ~/results/eval_<name>/.
+# Zero-shot table uses acc_norm for arc/hellaswag/piqa/openbookqa, acc otherwise.
+set -uo pipefail
+. ~/local/venvs/main/bin/activate
+HERE="$(cd "$(dirname "$0")" && pwd)"
+
+MODEL="${1:?usage: eval_model.sh <model> [name] [gpu]}"
+NAME="${2:-$(basename "$MODEL")}"
+GPU="${3:-0}"
+TASKS=arc_easy,arc_challenge,hellaswag,winogrande,piqa,boolq,openbookqa
+OUT="$HOME/results/eval_${NAME}"
+mkdir -p "$OUT"
+echo "[eval] model=$MODEL  name=$NAME  gpu=$GPU  -> $OUT"
+
+echo "[eval] 1/2  perplexity (wikitext2 + c4)..."
+CUDA_VISIBLE_DEVICES=$GPU python "$HERE/ppl_eval.py" "$MODEL" --out "$OUT/ppl.json"
+
+echo "[eval] 2/2  zero-shot (lm_eval + vllm)..."
+rm -rf "$OUT/zeroshot"
+CUDA_VISIBLE_DEVICES=$GPU lm_eval --model vllm \
+  --model_args "pretrained=$MODEL,tensor_parallel_size=1,gpu_memory_utilization=0.85,dtype=bfloat16,max_model_len=4096,trust_remote_code=True" \
+  --tasks "$TASKS" --batch_size auto --output_path "$OUT/zeroshot"
+
+echo "[eval] ===== $NAME ====="
+python - "$OUT" <<'PY'
+import glob, json, os, sys
+OUT = sys.argv[1]
+NORM = {"arc_easy", "arc_challenge", "hellaswag", "piqa", "openbookqa"}
+TASKS = ["arc_easy", "arc_challenge", "hellaswag", "winogrande", "piqa", "boolq", "openbookqa"]
+ppl = json.load(open(os.path.join(OUT, "ppl.json")))
+print(f"  WT2  {ppl['wikitext2']:.2f}")
+print(f"  C4   {ppl['c4']:.2f}")
+fs = glob.glob(os.path.join(OUT, "zeroshot", "**", "*.json"), recursive=True)
+r = (json.load(open(sorted(fs)[-1])).get("results", {})) if fs else {}
+vals = {}
+for t in TASKS:
+    x = r.get(t)
+    if x:
+        m = "acc_norm,none" if t in NORM else "acc,none"
+        vals[t] = x.get(m, x.get("acc,none")) * 100
+        print(f"  {t:14s} {vals[t]:.2f}")
+if vals:
+    print(f"  {'ZS avg':14s} {sum(vals.values())/len(vals):.2f}")
+PY

eval/ppl_eval.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""Sliding-window WT2 + C4 perplexity for one causal-LM checkpoint.
+
+Uses the checkpoint's own tokenizer, so it works for a plain HF model id or a
+local fake-quant checkpoint. Prints the two numbers and (optionally) writes them
+to a JSON file.
+
+Usage:
+  ppl_eval.py MODEL [--out ppl.json] [--seq 2048] [--stride 512] [--device cuda:0]
+"""
+import argparse
+import json
+
+import numpy as np
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+def wt2_text():
+    return "\n\n".join(load_dataset("wikitext", "wikitext-2-raw-v1", split="test")["text"])
+
+
+def c4_text(min_chars):
+    ds = load_dataset("allenai/c4", "en", split="validation", streaming=True)
+    parts, tot = [], 0
+    for ex in ds:
+        parts.append(ex["text"]); tot += len(ex["text"])
+        if tot >= min_chars:
+            break
+    return "\n\n".join(parts)
+
+
+def perplexity(model, tok, text, seq, stride, device):
+    ids = tok(text, return_tensors="pt").input_ids[0]
+    n = len(ids); nlls = []; prev_end = 0
+    for begin in range(0, n - 1, stride):
+        end = min(begin + seq, n)
+        trg_len = end - prev_end
+        chunk = ids[begin:end].unsqueeze(0).to(device)
+        with torch.no_grad():
+            logits = model(chunk, labels=chunk).logits
+        sl = logits[:, prev_end - begin:-1, :].contiguous()
+        lbl = chunk[:, prev_end - begin + 1:].contiguous()
+        loss = torch.nn.functional.cross_entropy(sl.view(-1, sl.size(-1)), lbl.view(-1))
+        nlls.append(loss.item() * trg_len)
+        prev_end = end
+        if end == n:
+            break
+    return float(np.exp(sum(nlls) / prev_end))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("model")
+    ap.add_argument("--out", default=None, help="write {wikitext2, c4} JSON here")
+    ap.add_argument("--seq", type=int, default=2048)
+    ap.add_argument("--stride", type=int, default=512)
+    ap.add_argument("--device", default="cuda:0")
+    ap.add_argument("--c4-chars", type=int, default=2_621_440)
+    args = ap.parse_args()
+
+    tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model, torch_dtype=torch.bfloat16, device_map=args.device,
+        trust_remote_code=True).eval()
+
+    res = {}
+    res["wikitext2"] = perplexity(model, tok, wt2_text(), args.seq, args.stride, args.device)
+    print(f"WT2 {res['wikitext2']:.4f}", flush=True)
+    res["c4"] = perplexity(model, tok, c4_text(args.c4_chars), args.seq, args.stride, args.device)
+    print(f"C4  {res['c4']:.4f}", flush=True)
+    if args.out:
+        with open(args.out, "w") as f:
+            json.dump(res, f, indent=2)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

qwen3_4b_tiled64/README.md

@@ -0,0 +1,32 @@
+# RCO quant pack
+
+Self-describing mixed-precision checkpoint. Everything needed to read it is in
+this directory (loader deps: `torch`, `safetensors`).
+
+```
+quant_pack-*.safetensors   per-layer tensors
+manifest.json              index + quant scheme + per-layer bit stats
+load_pack.py               standalone loader
+README.md                  this file
+```
+
+Per quantized layer `<name>` (block sizes / group_size in `manifest.json`):
+
+| tensor            | shape    | dtype | meaning                                       |
+|-------------------|----------|-------|-----------------------------------------------|
+| `<name>.tile_bits`| (Rb, Cb) | uint8 | bitwidth of each `row_block x col_block` tile |
+| `<name>.qweight`  | (out,in) | uint8 | integer codes, 0 .. 2^bw-1 per tile           |
+| `<name>.scales`   | (out, G) | bf16  | per output row x input group                  |
+| `<name>.zeros`    | (out, G) | bf16  | per output row x input group                  |
+| `<name>.weight`   | (out,in) | bf16  | fake-quant weight (optional)                  |
+
+Dequant (asymmetric, grouped along input columns): `w = scale * (qcode - zero)`.
+Bitwidth of element (r, c): `tile_bits[r // row_block, c // col_block]`.
+
+```python
+python load_pack.py .                                  # summary
+from load_pack import load_quant_pack, dequant
+man, get = load_quant_pack(".")
+L = get("model.layers.0.self_attn.q_proj")
+W = dequant(L["qweight"], L["scales"], L["zeros"], man["quant_scheme"]["group_size"])
+```

qwen3_4b_tiled64/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

qwen3_4b_tiled64/config.json

@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

qwen3_4b_tiled64/generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.6.2"
+}

qwen3_4b_tiled64/load_pack.py

@@ -0,0 +1,49 @@
+"""Standalone loader for an RCO quant pack. Dependencies: torch, safetensors.
+
+    from load_pack import load_quant_pack, dequant
+    man, get = load_quant_pack(".")            # pack dir
+    L = get("model.layers.0.self_attn.q_proj") # tile_bits/qweight/scales/zeros/weight
+    W = dequant(L["qweight"], L["scales"], L["zeros"], man["quant_scheme"]["group_size"])
+
+Dequant: w = scale * (qcode - zero). Tile of element (r,c) is
+tile_bits[r // row_block, c // col_block]  (block sizes in manifest.json).
+"""
+import glob, json, os
+import torch
+from safetensors import safe_open
+
+
+def dequant(qweight, scales, zeros, group_size):
+    q = qweight.to(torch.float32)
+    s = scales.to(torch.float32).repeat_interleave(group_size, dim=1)
+    z = zeros.to(torch.float32).repeat_interleave(group_size, dim=1)
+    return (s * (q - z)).to(torch.bfloat16)
+
+
+def load_quant_pack(pack_dir="."):
+    manifest = json.loads(open(os.path.join(pack_dir, "manifest.json")).read())
+    shards = manifest.get("shards") or sorted(
+        os.path.basename(p) for p in glob.glob(os.path.join(pack_dir, "*.safetensors")))
+    handles, key2file = {}, {}
+    for fn in shards:
+        p = os.path.join(pack_dir, fn)
+        h = safe_open(p, framework="pt"); handles[p] = h
+        for k in h.keys():
+            key2file[k] = p
+
+    def get_layer(name):
+        out = {}
+        for field in ("tile_bits", "qweight", "scales", "zeros", "weight"):
+            k = name + "." + field
+            if k in key2file:
+                out[field] = handles[key2file[k]].get_tensor(k)
+        return out
+
+    return manifest, get_layer
+
+
+if __name__ == "__main__":
+    import sys
+    m, get = load_quant_pack(sys.argv[1] if len(sys.argv) > 1 else ".")
+    print("model", m["model"], "| layers", m["n_layers"],
+          "| avg bits", round(m["overall_avg_bits"], 4), "| scheme", m["quant_scheme"])

qwen3_4b_tiled64/manifest.json

@@ -0,0 +1,4431 @@
+{
+  "format": "rco-quant-pack/v1",
+  "model": "Qwen/Qwen3-4B",
+  "dequant_formula": "w = scale * (qcode - zero)",
+  "quant_scheme": {
+    "asymmetric": true,
+    "quant_axis": "input-group",
+    "group_size": 64,
+    "row_block_size": 64,
+    "col_block_size": 64,
+    "bitwidths": [
+      2,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8
+    ],
+    "qcode_range": "0 .. 2**bw - 1 per tile (see tile_bits)"
+  },
+  "fields": [
+    "tile_bits",
+    "qweight",
+    "scales",
+    "zeros"
+  ],
+  "n_layers": 252,
+  "overall_avg_bits": 2.249978585762723,
+  "target_avg_bits": 2.25,
+  "actual_avg_bits": 2.249978542327881,
+  "shards": [
+    "quant_pack-00001.safetensors"
+  ],
+  "layers": {
+    "model.layers.0.self_attn.q_proj": {
+      "out": 4096,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        64,
+        40
+      ],
+      "avg_bits": 2.2320313453674316,
+      "tile_bit_hist": {
+        "2": 2039,
+        "3": 457,
+        "4": 55,
+        "5": 9
+      }
+    },
+    "model.layers.0.self_attn.k_proj": {
+      "out": 1024,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        16,
+        40
+      ],
+      "avg_bits": 2.518749952316284,
+      "tile_bit_hist": {
+        "2": 375,
+        "3": 209,
+        "4": 46,
+        "5": 9,
+        "6": 1
+      }
+    },
+    "model.layers.0.self_attn.v_proj": {
+      "out": 1024,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        16,
+        40
+      ],
+      "avg_bits": 2.9234375953674316,
+      "tile_bit_hist": {
+        "2": 245,
+        "3": 242,
+        "4": 114,
+        "5": 35,
+        "6": 4
+      }
+    },
+    "model.layers.0.self_attn.o_proj": {
+      "out": 2560,
+      "in_features": 4096,
+      "group_size": 64,
+      "groups": 64,
+      "grid_shape": [
+        40,
+        64
+      ],
+      "avg_bits": 2.5035157203674316,
+      "tile_bit_hist": {
+        "2": 1601,
+        "3": 696,
+        "4": 202,
+        "5": 57,
+        "6": 3,
+        "8": 1
+      }
+    },
+    "model.layers.0.mlp.gate_proj": {
+      "out": 9728,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        152,
+        40
+      ],
+      "avg_bits": 2.28092098236084,
+      "tile_bit_hist": {
+        "2": 4587,
+        "3": 1292,
+        "4": 187,
+        "5": 14
+      }
+    },
+    "model.layers.0.mlp.up_proj": {
+      "out": 9728,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        152,
+        40
+      ],
+      "avg_bits": 2.2879934310913086,
+      "tile_bit_hist": {
+        "2": 4579,
+        "3": 1274,
+        "4": 204,
+        "5": 23
+      }
+    },
+    "model.layers.0.mlp.down_proj": {
+      "out": 2560,
+      "in_features": 9728,
+      "group_size": 64,
+      "groups": 152,
+      "grid_shape": [
+        40,
+        152
+      ],
+      "avg_bits": 2.50016450881958,
+      "tile_bit_hist": {
+        "2": 3691,
+        "3": 1838,
+        "4": 457,
+        "5": 88,
+        "6": 5,
+        "7": 1
+      }
+    },
+    "model.layers.1.self_attn.q_proj": {
+      "out": 4096,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        64,
+        40
+      ],
+      "avg_bits": 2.1195311546325684,
+      "tile_bit_hist": {
+        "2": 2288,
+        "3": 241,
+        "4": 28,
+        "5": 3
+      }
+    },
+    "model.layers.1.self_attn.k_proj": {
+      "out": 1024,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        16,
+        40
+      ],
+      "avg_bits": 2.3453125953674316,
+      "tile_bit_hist": {
+        "2": 467,
+        "3": 129,
+        "4": 40,
+        "5": 4
+      }
+    },
+    "model.layers.1.self_attn.v_proj": {
+      "out": 1024,
+      "in_features": 2560,
+      "group_size": 64,
+      "groups": 40,
+      "grid_shape": [
+        16,
+        40
+      ],
+      "avg_bits": 2.484375,
+      "tile_bit_hist": {
+        "2": 393,
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