Instructions to use bartowski/WizardCoder-33B-V1.1-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/WizardCoder-33B-V1.1-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/WizardCoder-33B-V1.1-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/WizardCoder-33B-V1.1-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bartowski/WizardCoder-33B-V1.1-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/WizardCoder-33B-V1.1-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/WizardCoder-33B-V1.1-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/WizardCoder-33B-V1.1-exl2
- SGLang
How to use bartowski/WizardCoder-33B-V1.1-exl2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "bartowski/WizardCoder-33B-V1.1-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/WizardCoder-33B-V1.1-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "bartowski/WizardCoder-33B-V1.1-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/WizardCoder-33B-V1.1-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bartowski/WizardCoder-33B-V1.1-exl2 with Docker Model Runner:
docker model run hf.co/bartowski/WizardCoder-33B-V1.1-exl2
File size: 2,312 Bytes
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metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: WizardCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.799
verified: false
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of WizardCoder-33B-V1.1
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/WizardLM/WizardCoder-33B-V1.1
<a href="https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2/tree/2_4">2.4 bits per weight</a>
<a href="https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2/tree/3_0">3.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2/tree/3_5">3.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2/tree/6_5">6.5 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `WizardCoder-33B-V1.1-exl2`:
```shell
mkdir WizardCoder-33B-V1.1-exl2
huggingface-cli download bartowski/WizardCoder-33B-V1.1-exl2 --local-dir WizardCoder-33B-V1.1-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir WizardCoder-33B-V1.1-exl2
huggingface-cli download bartowski/WizardCoder-33B-V1.1-exl2 --revision 4_0 --local-dir WizardCoder-33B-V1.1-exl2 --local-dir-use-symlinks False
```
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