WizardLM 70B V1.0 – EXL2

Model creator: WizardLM
FP32 Original model used for quantization: WizardLM 70B V1.0 – float32
FP16 Model used for quantization: WizardLM 70B V1.0-HF – float16 of WizardLM 70B V1.0
BF16 Model used for quantization: WizardLM 70B V1.0-BF16 – bfloat16 of WizardLM 70B V1.0

Models available:

Link	BITS (-b)	HEAD BITS (-hb)	MEASU-REMENT LENGTH (-ml)	LENGTH (-l)	CAL DATASET (-c)	Size	V.	Max Context Length	Base Model	Layers	VRAM Min	VRAM Max	PPL**	Comments
here	4.0	6	2048	2048	0000.parquet*	33GB	0.0.2	4096	FP32	80	39GB	44GB	4.15234375	Good results
here	4.0	6	2048	2048	0000.parquet*	33GB	0.0.2	4096	BF16	80	39GB	44GB	4.2421875	Model suffers from poor prompt understanding and logic is affected
here	4.0	8	2048	2048	0000.parquet*	35GB	0.0.2	4096	FP16	80	39GB	44GB	4.24609375	Model suffers from poor prompt understanding and logic is affected
here	5.0	6	2048	2048	0000.parquet*	41GB	0.0.2	4096	FP32	80	47GB	52GB	4.06640625	Best so far. Good results
here	5.0	8	2048	2048	0000.parquet*	44GB	0.0.2	4096	FP16	80	48GB	52GB	4.09765625	Model suffers from poor prompt understanding and logic is affected
here	5.0	6	2048	2048	0000.parquet*	44GB	0.0.1	4096	FP16	80	48GB	52GB	4.0625	Model suffers from poor prompt understanding and logic is affected
here	5.0	6	2048	2048	0000.parquet*	41GB	0.0.2	4096	BF16	80	47GB	52GB	4.09765625	Model suffers from poor prompt understanding and logic is affected
here	6.0	6	2048	2048	0000.parquet*	49GB	0.0.2	4096	FP16	80	56GB	60GB	4.0703125	Model suffers from poor prompt understanding and logic is affected

* wikitext-2-raw-v1

** Evaluated with text-generation-webui ExLlama v0.0.2 on wikitext-2-raw-v1 (stride 512 and max_length 0). For reference, TheBloke_WizardLM-70B-V1.0-GPTQ_gptq-4bit-32g-actorder_True has a score of 4.1015625 in perplexity.

Description:

This repository contains EXL2 model files for WizardLM's WizardLM 70B V1.0.

EXL2 is a new format used by ExLlamaV2 – https://github.com/turboderp/exllamav2. EXL2 is based on the same optimization method as GPTQ. The format allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight.

Prompt template (official):

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:

Prompt template (suggested):

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER:
{prompt}
ASSISTANT:

Quantization process:

Original Model	→	(optional) float16 or bfloat16 Model*	→	Safetensors Model**	→	EXL2 Model
WizardLM 70B V1.0	→	WizardLM 70B V1.0-HF*	→	Safetensors**	→	EXL2

Example to convert WizardLM-70B-V1.0-HF to EXL2 4.0 bpw with 6-bit head:

mkdir -p ~/EXL2/WizardLM-70B-V1.0-HF_4bit # Create the output directory
python convert.py -i ~/float16_safetensored/WizardLM-70B-V1.0-HF -o ~/EXL2/WizardLM-70B-V1.0-HF_4bit -c ~/EXL2/0000.parquet -b 4.0 -hb 6

* Use the following script to convert your local pytorch_model bin files to float16 (you can also choose bfloat16) + safetensors all in one go:

https://github.com/oobabooga/text-generation-webui/blob/main/convert-to-safetensors.py (best for sharding and float16/FP16 or bfloat16/BF16 conversion)

Example to convert WizardLM 70B V1.0 directly to float16 safetensors in 10GB shards:

python convert-to-safetensors.py ~/original/WizardLM-70B-V1.0 --output ~/float16_safetensored/WizardLM-70B-V1.0 --max-shard-size 10GB

Use --bf16 if you'd like to try bfloat16 instead, but note that there are concerns about quantization quality – https://github.com/turboderp/exllamav2/issues/30#issuecomment-1719009289

** Use any one of the following scripts to convert your local pytorch_model bin files to safetensors: