Update README.md

README.md

@@ -46,12 +46,28 @@ Q6_K_M : attn_v = q8_0 ffn_d = q8_0
    FLAGS="--token-embedding-type Q4_K --output-tensor-type Q6_K --layer-types-high"
 ```
 
+Q4_P_H is also available.  This quant pads FFN dimension to even 256 so K quants can be used with it.  Both the Q4_K_H
+and Q6_K_H quant will replace specified layer quant with legacy quants for FFN tensors while Q4_P_H will use the exactly specified
+K layer quants.  Eliminating the legacy quants makes the size and performance more efficient since all layers
+use K quants.
+```
+   LAYER_TYPES='[
+   [0 ,"Q6_K_M"],[1 ,"Q5_K_L"],[2 ,"Q5_K_M"],[3 ,"Q5_K_S"],[4 ,"Q4_K_L"],[5 ,"Q4_K_M"],[6 ,"Q4_K_S"],[7 ,"Q4_K_M"],
+   [8 ,"Q4_K_S"],[9 ,"Q4_K_S"],[10,"Q4_K_S"],[11,"Q4_K_S"],[12,"Q4_K_S"],[13,"Q4_K_S"],[14,"Q4_K_S"],[15,"Q4_K_S"],
+   [16,"Q4_K_M"],[17,"Q4_K_S"],[18,"Q4_K_M"],[19,"Q4_K_S"],[20,"Q4_K_M"],[21,"Q4_K_S"],[22,"Q4_K_M"],[23,"Q4_K_S"],
+   [24,"Q4_K_M"],[25,"Q4_K_M"],[26,"Q4_K_M"],[27,"Q4_K_M"],[28,"Q4_K_L"],[29,"Q4_K_L"],[30,"Q4_K_L"],[31,"Q5_K_M"],
+   [32,"Q5_K_M"],[33,"Q5_K_M"],[34,"Q5_K_L"],[35,"Q5_K_L"],[36,"Q5_K_L"],[37,"Q6_K_S"],[38,"Q6_K_M"],[39,"Q6_K_L"]
+   ]'
+   FLAGS="--token-embedding-type Q4_K --output-tensor-type Q6_K --layer-types-high  --tensor-pad [[13696,13824],[27392,27648,2]] --override-kv glm4.feed_forward_length=int:13824"
+```
+
 Comparison:
 
 Quant |  size  |  PPL |   Comment
 ---------|---------|------|-----------
 IQ4_XS   | 5.3e9   | 14.8  | -
-Q4_K_H   | 6.6e9   | 14.9  | Hybrid quant with Q4_K embedding Q6_K_output
+Q4_P_H   | 6.3e9   | 14.8  | Hybrid quant with Q4_K embedding Q6_K output padded FFN tensors for K quants
+Q4_K_H   | 6.6e9   | 14.9  | Hybrid quant with Q4_K embedding Q6_K output
 Q6_K  | 8.3e9 | 14.7 |  Q6_K with default embedding and output, unstable with greedy sampling, poor performance on eval prompts
 Q6_K_H |  7.3e9  |  14.8  | Hybrid quant with Q6_K embedding Q6_K output, stable with greedy sampling, excellent performance on eval prompts
 
@@ -68,6 +84,18 @@ solution of a complex problem, compared against a "dumber" thinking model which
 suggests the RL training for the underlying model might have rewarded efficient solutions more than inefficient ones (hypothesis, could also just
 be coincidence it happens to be "smart" on a couple tricky problems in the eval test set).
 
+The Q4_P_H quant is an improved Q4 quant enabling it to use K quants for FFN tensors instead of the fallback legacy quants.  It does
+very well on eval test set.  It is smallest available high performance hybrid quant for the model.
+
+The model can be speculated using Qwen3 0.6B if the inference engine can support dynamic vocab translation between
+draft and target models.  Approx performance using a downstream speculator with llama.cpp on a 4070 (12G VRAM) with layers and
+context fully in GPU:
+
+Q     |  QKV  |  ND  |   NKV  |  gen tps   | Comment
+------|-------|------|--------|------------|---------
+Q4_P_H|  F16  |   0  |  32k   |   66       |   No draft
+Q4_P_H|  F16  |   3  |  31k   |   81       |   Spec 3
+
 This is one of the strongest general reasoning models I have experienced to date as of 7/21/2025 independent of size, compared against both QwQ,
 R1 distills of Qwen 2.5 models, and Qwen 3.  However testing with some code problems show it is **extremely weak** on code generation problems.
 
@@ -79,6 +107,7 @@ Math benchmarks for the model are given here: https://huggingface.co/spaces/stea
 | Link | Type | Size/e9 B | Notes |
 |------|------|-----------|-------|
 | [GLM-Z1-9B-0414.Q4_K_H.gguf](https://huggingface.co/steampunque/GLM-Z1-9B-0414-Hybrid-GGUF/resolve/main/GLM-Z1-9B-0414.Q4_K_H.gguf) | Q4_K_H | 6.6e9 B | 1.7B smaller than Q6_K with much better performance |
+| [GLM-Z1-9B-0414.Q4_P_H.gguf](https://huggingface.co/steampunque/GLM-Z1-9B-0414-Hybrid-GGUF/resolve/main/GLM-Z1-9B-0414.Q4_P_H.gguf) | Q4_P_H | 6.3e9 B | 2B smaller than Q6_K with much better performance |
 | [GLM-Z1-9B-0414.Q6_K_H.gguf](https://huggingface.co/steampunque/GLM-Z1-9B-0414-Hybrid-GGUF/resolve/main/GLM-Z1-9B-0414.Q6_K_H.gguf) | Q6_K_H | 7.3e9 B | 1B smaller than Q6_K with much better performance |
  
 A discussion thread about the hybrid layer quant approach can be found here on the llama.cpp git repository: