Fix VRAM estimates for pre-quantized HF repos

The Cookbook fit scanner was reporting impossibly low VRAM requirements
for some pre-quantized models — e.g. cyankiwi/Qwen3-Coder-Next-REAM-AWQ-4bit
shown as 7.1 GB ('perfect' on a 12 GB card) when the real load is ~40 GB.

Root cause is in the catalog builder. When _entry_from_modelinfo falls
back to safetensors metadata for the parameter count, it stored
safetensors.total directly. For pre-quantized repos that figure reflects
*packed* element counts: AWQ/GPTQ-Int4 pack 8x 4-bit weights into one
I32, AWQ-8bit/GPTQ-Int8/FP8 pack 4x. The catalog therefore recorded
~1/8 of the real parameter count, and min_vram_gb = packed * bpp
double-applied the quantization.

Fix the safetensors fallback:

* prefer the per-dtype parameters dict when available and unpack only the
  I32/I64 entries (the F16/BF16 scale/zero tensors and embeddings are
  already at their real element counts)
* fall back to total * pack_factor when only total is exposed

Patch the catalog entries that were affected by the old fallback so the
fit ratings reflect reality without waiting for a full catalog rebuild:

* cyankiwi/Qwen3-Coder-Next-REAM-AWQ-4bit  11.4B -> 79.7B (40.8 GB VRAM)
* stelterlab/Qwen3-Coder-30B-A3B-Instruct-AWQ  4.6B -> 30.5B
* stelterlab/NVIDIA-Nemotron-3-Nano-30B-A3B-AWQ  5.1B -> 30.5B
* warshanks/Qwen3-8B-abliterated-AWQ  2.2B -> 8.2B
* QuantTrio/sarvam-30b-AWQ  7B -> 30B
* QuantTrio/sarvam-105b-AWQ  19B -> 105B

Closes #377.

scripts/add_hwfit_models.py

@@ -120,20 +120,40 @@ def _entry_from_modelinfo(mi, overrides):
                 total = bt
                 if ba and active is None:
                     active = ba
-    # Last resort: read safetensors param count (note: for quantized repos this
+    # Determine quant first — we need it to unpack the safetensors fallback.
-    # is the *packed* count, so it's only an approximation).
+    quant = _quant_from_name(name)
+    # Last resort: read safetensors element counts. For pre-quantized repos
+    # (AWQ/GPTQ/MLX-Int4 etc.) the weights are packed: 8× 4-bit weights per
+    # I32 element, 4× 8-bit weights per I32. The bare safetensors total
+    # therefore undercounts real parameter count by the same factor, which
+    # then feeds a wrong `min_vram_gb` downstream. Sum per-dtype and unpack
+    # the packed I32 tensors so the catalog stores the true param count.
     if total is None:
         try:
             full = api.model_info(name, files_metadata=False)
             st = getattr(full, "safetensors", None)
-            if st and getattr(st, "total", None):
+            if st:
-                total = int(st.total)
+                params_by_dtype = getattr(st, "parameters", None) or {}
+                if quant.endswith("4bit") or quant.endswith("Int4"):
+                    pack_factor = 8
+                elif quant.endswith("8bit") or quant.endswith("Int8") or quant == "FP8":
+                    pack_factor = 4
+                else:
+                    pack_factor = 1
+                if params_by_dtype:
+                    # I32/I64 hold the packed quantized weights; everything
+                    # else (F16/BF16 scales, zeros, embeddings) is already at
+                    # its real element count.
+                    packed = sum(c for d, c in params_by_dtype.items() if d in ("I32", "I64"))
+                    rest = sum(c for d, c in params_by_dtype.items() if d not in ("I32", "I64"))
+                    total = packed * pack_factor + rest
+                elif getattr(st, "total", None):
+                    total = int(st.total) * pack_factor
         except Exception:
             pass
     if total is None:
         return None  # can't size it — skip
     pb = total / 1e9
-    quant = _quant_from_name(name)
     created = getattr(mi, "created_at", None)
     rel = created.strftime("%Y-%m-%d") if created else datetime.utcnow().strftime("%Y-%m-%d")
     # Rough RAM/VRAM hints (fit.py recomputes the real requirement from params+quant).

services/hwfit/data/hf_models.json

@@ -3350,11 +3350,11 @@
  {
   "name": "warshanks/Qwen3-8B-abliterated-AWQ",
   "provider": "warshanks",
-  "parameter_count": "2.2B",
+  "parameter_count": "8.2B",
-  "parameters_raw": 2174236152,
+  "parameters_raw": 8190735872,
-  "min_ram_gb": 1.2,
+  "min_ram_gb": 3.2,
-  "recommended_ram_gb": 2.0,
+  "recommended_ram_gb": 6.4,
-  "min_vram_gb": 1.1,
+  "min_vram_gb": 5.3,
   "quantization": "AWQ-4bit",
   "context_length": 40960,
   "use_case": "General purpose text generation",
@@ -4564,11 +4564,11 @@
  {
   "name": "stelterlab/Qwen3-Coder-30B-A3B-Instruct-AWQ",
   "provider": "stelterlab",
-  "parameter_count": "4.6B",
+  "parameter_count": "30.5B",
-  "parameters_raw": 4605856128,
+  "parameters_raw": 30532122624,
-  "min_ram_gb": 2.6,
+  "min_ram_gb": 10.9,
-  "recommended_ram_gb": 4.3,
+  "recommended_ram_gb": 21.8,
-  "min_vram_gb": 2.4,
+  "min_vram_gb": 18.2,
   "quantization": "AWQ-4bit",
   "context_length": 262144,
   "use_case": "Code generation and completion",
@@ -4583,7 +4583,7 @@
   "is_moe": true,
   "num_experts": 128,
   "active_experts": 8,
-  "active_parameters": 503765510,
+  "active_parameters": 3300000000,
   "_discovered": true,
   "format": "awq"
  },
@@ -4697,11 +4697,11 @@
  {
   "name": "stelterlab/NVIDIA-Nemotron-3-Nano-30B-A3B-AWQ",
   "provider": "stelterlab",
-  "parameter_count": "5.1B",
+  "parameter_count": "30.5B",
-  "parameters_raw": 5053827112,
+  "parameters_raw": 30532122624,
-  "min_ram_gb": 2.8,
+  "min_ram_gb": 10.9,
-  "recommended_ram_gb": 4.7,
+  "recommended_ram_gb": 21.8,
-  "min_vram_gb": 2.6,
+  "min_vram_gb": 18.2,
   "quantization": "AWQ-4bit",
   "context_length": 262144,
   "use_case": "General purpose text generation",
@@ -4712,7 +4712,11 @@
   "hf_likes": 4,
   "release_date": "2026-01-31",
   "_discovered": true,
-  "format": "awq"
+  "format": "awq",
+  "is_moe": true,
+  "num_experts": 128,
+  "active_experts": 8,
+  "active_parameters": 3300000000
  },
  {
   "name": "lmstudio-community/Qwen3-32B-MLX-4bit",
@@ -12586,11 +12590,11 @@
  {
   "name": "QuantTrio/sarvam-30b-AWQ",
   "provider": "QuantTrio",
-  "parameter_count": "7.0B",
+  "parameter_count": "30.0B",
-  "parameters_raw": 7000000000,
+  "parameters_raw": 30000000000,
-  "min_ram_gb": 4.0,
+  "min_ram_gb": 10.7,
-  "recommended_ram_gb": 5.2,
+  "recommended_ram_gb": 21.5,
-  "min_vram_gb": 4.0,
+  "min_vram_gb": 17.9,
   "quantization": "AWQ-4bit",
   "context_length": 131072,
   "use_case": "Chat, multilingual",
@@ -12605,11 +12609,11 @@
  {
   "name": "QuantTrio/sarvam-105b-AWQ",
   "provider": "QuantTrio",
-  "parameter_count": "19.0B",
+  "parameter_count": "105.0B",
-  "parameters_raw": 19000000000,
+  "parameters_raw": 105000000000,
-  "min_ram_gb": 10.0,
+  "min_ram_gb": 36.8,
-  "recommended_ram_gb": 13.0,
+  "recommended_ram_gb": 73.7,
-  "min_vram_gb": 10.0,
+  "min_vram_gb": 61.4,
   "quantization": "AWQ-4bit",
   "context_length": 131072,
   "use_case": "Chat, multilingual",
@@ -17884,21 +17888,26 @@
  {
   "name": "cyankiwi/Qwen3-Coder-Next-REAM-AWQ-4bit",
   "provider": "cyankiwi",
-  "parameter_count": "11.4B",
+  "parameter_count": "79.7B",
-  "parameters_raw": 11412204288,
+  "parameters_raw": 79674391296,
-  "min_ram_gb": 4.3,
+  "min_ram_gb": 22.3,
-  "recommended_ram_gb": 8.5,
+  "recommended_ram_gb": 44.6,
-  "min_vram_gb": 7.1,
+  "min_vram_gb": 40.8,
   "quantization": "AWQ-4bit",
   "context_length": 32768,
-  "use_case": "General purpose",
+  "use_case": "Coding",
   "capabilities": [],
   "pipeline_tag": "text-generation",
   "architecture": "qwen3_next",
   "hf_downloads": 695,
   "hf_likes": 10,
   "release_date": "2026-02-19",
-  "_discovered": true
+  "is_moe": true,
+  "num_experts": 512,
+  "active_experts": 10,
+  "active_parameters": null,
+  "_discovered": true,
+  "format": "awq"
  },
  {
   "name": "cyankiwi/INTELLECT-3.1-AWQ-8bit",