base_model: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
inference: false
license: llama2
model_creator: https://huggingface.co/Phind
model_name: Phind-Codellama-34B-v2
model_type: llama
quantized_by: latimar
Phind-CodeLlama-34B-v2 EXL2
Weights of Phind-CodeLlama-34B-v2 converted to EXL2 format.
Converted with the ExllamaV2 convert.py script, exllamav2 commit
Original model in full weights achieves 73.8 HumanEval score. Here are EXL2 quants scores:
| BPW (hb=8) | HumanEval | Evol-Ins PPL | Wiki PPL | File Size (Gb) |
|---|---|---|---|---|
| 2.55 | 40.24 | 2.0944 | 18.9843 | 10.62 |
| 2.8 | 63.41 | 2.0814 | 17.6326 | 11.58 |
| 3.0 | 66.46 | 2.0600 | 11.2096 | 12.36 |
| 4.625 | 70.12 | 2.0401 | 6.7243 | 18.63 |
| 4.8 | 70.73 | 2.0361 | 6.7263 | 19.32 |
Downloads
If you just do git clone you will get weights of all the quants, which is probably not
what you want. You need to download (and put in the same dir) the following common files:
And the weights of a particular quant: all safetensors files + model.safetensors.index.json file from the quant directory.
Either download these files via the Web UI, or, e.g., with curl:
mkdir phind-2.55
cd phind-2.55
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/generation_config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/blob/main/special_tokens_map.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/tokenizer.model
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/tokenizer_config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/model.safetensors.index.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/output-00001-of-00002.safetensors
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/output-00002-of-00002.safetensors
Datasets used for calibration and PPL measurement
Conversion
Conversion arguments:
convert.py -i ${MODEL_DIR_FP16} -o ${WIP_DIR} -cf ${MODEL_DIR_EXL} -c ${CALIBRATION_DATASET} -r 200 -mr 32 -l 4096 -ml 4096 -hb 8 -b ${BPW}
2.55 quant was converted using even more raws: -r 400 -mr 64
Perplexity
Perplexity was measured with test_inference.py script:
test_inference.py -m ${MODEL_DIR_EXL} -ed ${PPL_DATASET}
Human-Eval
Evaluation
Samples for the Human-Eval scores of EXL2 quants were generated with exl2.human-eval.py script:
python exl2.human-eval.py -m ${MODEL_DIR_EXL2} -c 4096 -o ${BPW}-samples.jsonl
Human-Eval samples of NF4/INT8 quants were generated with tf.human-eval.py script:
python tf.human-eval.py -m ${MODEL_DIR_FP16} -o nf4-samples.jsonl
Comparison
Phind says that the original model in full weights achieves 73.8 Human-Eval score. NF4 quant gives me 70.73
WizardCoder models claimed Human-Eval scores (full weights):
| Model | Score |
|---|---|
| WizardCoder-Python-34B-V1.0 | 73.2 |
| WizardCoder-Python-13B-V1.0 | 64.0 |
Vanilla Mistral-7B INT8 scores 27.43
EXL2 3.2-bpw quant of this model by firelzrd scores 60.97.