latimar commited on
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Add 2.8 quant weights

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README.md CHANGED
@@ -20,9 +20,9 @@ exllamav2 [commit](https://github.com/turboderp/exllamav2/commit/31f31e1b08eeccf
20
  | BPW (hb=8) | Human-Eval | Evol-Ins PPL | Wiki PPL | File Size (Gb) |
21
  | ----------- | ----------- | ------------ | ---------- | -------------- |
22
  | 2.55 | 0.402439 | 2.0944 | 18.9843 | 10.62 |
 
23
  | 3.0 | 0.664634 | 2.0600 | 11.2096 | 12.36 |
24
  | 4.625 | 0.701219 | 2.0401 | 6.7243 | 18.63 |
25
- | 5.0 | 0.670731 | 2.0391 | 6.6956 | 20.09 |
26
 
27
  ## Datasets used for calibration and PPL measurement
28
 
@@ -52,6 +52,12 @@ test_inference.py -m ${MODEL_DIR_EXL} -ed ${PPL_DATASET}
52
 
53
  For the point of reference, Phind says that the original model achieves **73.8** Human-Eval score.
54
 
 
 
 
 
 
 
55
  Unfortunately, FP16/INT8 weights of this model won't fit on my RTX 4090, but FP16 quantized to NF4 fits,
56
  so I generated samples with [tf.human-eval.py](https://github.com/epicfilemcnulty/llm-tools/blob/main/eval/tf.human-eval.py) script:
57
  ```
@@ -60,8 +66,5 @@ python tf.human-eval.py -m ${MODEL_DIR_FP16} -o nf4-samples.jsonl
60
 
61
  NF4 variant gives us **0.70731707**
62
 
63
- Samples for the Human-Eval scores of EXL2 quants were generated with [exl2.human-eval.py](https://github.com/epicfilemcnulty/llm-tools/blob/main/eval/exl2.human-eval.py)
64
- script like this:
65
- ```
66
- python exl2.human-eval.py -m ${MODEL_DIR_EXL2} -c 4096 -o ${BPW}-samples.jsonl
67
- ```
 
20
  | BPW (hb=8) | Human-Eval | Evol-Ins PPL | Wiki PPL | File Size (Gb) |
21
  | ----------- | ----------- | ------------ | ---------- | -------------- |
22
  | 2.55 | 0.402439 | 2.0944 | 18.9843 | 10.62 |
23
+ | 2.8 | 0.634146 | 2.0814 | 17.6326 | 11.58 |
24
  | 3.0 | 0.664634 | 2.0600 | 11.2096 | 12.36 |
25
  | 4.625 | 0.701219 | 2.0401 | 6.7243 | 18.63 |
 
26
 
27
  ## Datasets used for calibration and PPL measurement
28
 
 
52
 
53
  For the point of reference, Phind says that the original model achieves **73.8** Human-Eval score.
54
 
55
+ Samples for the Human-Eval scores of EXL2 quants were generated with [exl2.human-eval.py](https://github.com/epicfilemcnulty/llm-tools/blob/main/eval/exl2.human-eval.py)
56
+ script:
57
+ ```
58
+ python exl2.human-eval.py -m ${MODEL_DIR_EXL2} -c 4096 -o ${BPW}-samples.jsonl
59
+ ```
60
+
61
  Unfortunately, FP16/INT8 weights of this model won't fit on my RTX 4090, but FP16 quantized to NF4 fits,
62
  so I generated samples with [tf.human-eval.py](https://github.com/epicfilemcnulty/llm-tools/blob/main/eval/tf.human-eval.py) script:
63
  ```
 
66
 
67
  NF4 variant gives us **0.70731707**
68
 
69
+ As another reference, [EXL2 3.2-bpw quant](https://huggingface.co/firelzrd/Phind-CodeLlama-34B-v2-exl2/tree/3_2-bpw) of this model by [firelzrd](https://huggingface.co/firelzrd)
70
+ yields **0.609756** Human-Eval score.