---
inference: false
license: llama2
model_creator: WizardLM
model_link: https://huggingface.co/WizardLM/WizardLM-70B-V1.0
model_name: WizardLM 70B V1.0
model_type: llama
quantized_by: Thireus
---

# WizardLM 70B V1.0 – EXL2
- Model creator: [WizardLM](https://huggingface.co/WizardLM)
- Original model: [WizardLM 70B V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0)
- Model used for quantization: [WizardLM 70B V1.0-HF](https://huggingface.co/simsim314/WizardLM-70B-V1.0-HF) – float16 of [WizardLM 70B V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0)

## Models available in this repository

| Link | BITS (-b) | HEAD BITS (-hb) | MEASUREMENT LENGTH (-ml) | LENGTH (-l) | CAL DATASET (-c) | Size | ExLlama | Max Context Length |
| ------ | --------- | --------------- | ------------------------ | ----------- | ---------------- | ---- | ------- | ------------------ |
| [here](https://huggingface.co/Thireus/WizardLM-70B-V1.0-HF-4.0bpw-h6-exl2/) | 4.0 | 6 | 2048 | 2048 | [0000.parquet](https://huggingface.co/datasets/wikitext/tree/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train)* | 35GB | [v2](https://github.com/turboderp/exllamav2) | 4096 | 
| [here](https://huggingface.co/Thireus/WizardLM-70B-V1.0-HF-5.0bpw-h6-exl2/) | 5.0 | 6 | 2048 | 2048 | [0000.parquet](https://huggingface.co/datasets/wikitext/tree/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train)* | 44GB | [v2](https://github.com/turboderp/exllamav2) | 4096 | 
| _coming soon..._ | 6.0 | 6 | 2048 | 2048 | [0000.parquet](https://huggingface.co/datasets/wikitext/tree/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train)* | ...GB | [v2](https://github.com/turboderp/exllamav2) | 4096 | 

\* wikitext-2-raw-v1

## Description:

_This repository contains EXL2 model files for [WizardLM's WizardLM 70B V1.0](https://huggingface.co/WizardLM/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 but recommended) Float16 Model* | → | Safetensor Model** | → | EXL2 Model |
| -------------- | --- | ------------- | --- | ---------------- | --- | ---------- |
| [WizardLM 70B V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0) | → | [WizardLM 70B V1.0-HF](https://huggingface.co/simsim314/WizardLM-70B-V1.0-HF)* | → | Safetensor** | → | 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](https://huggingface.co/WizardLM/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:

- https://github.com/turboderp/exllamav2/blob/master/util/convert_safetensors.py (official ExLlamaV2)
- https://huggingface.co/Panchovix/airoboros-l2-70b-gpt4-1.4.1-safetensors/blob/main/bin2safetensors/convert.py (recommended)
- https://gist.github.com/epicfilemcnulty/1f55fd96b08f8d4d6693293e37b4c55e#file-2safetensors-py

## Further reading:

- https://mlabonne.github.io/blog/posts/Introduction_to_Weight_Quantization.html