Instructions to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WizardLMTeam/WizardCoder-Python-34B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardCoder-Python-34B-V1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0

SGLang

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 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 "WizardLMTeam/WizardCoder-Python-34B-V1.0" \
    --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": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"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 "WizardLMTeam/WizardCoder-Python-34B-V1.0" \
        --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": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
```

WizardCoder-Python-34B-V1.0 / README.md

WizardLM

Update README.md

e79a3da almost 3 years ago

4.74 kB

license: llama2

We released WizardCoder-15B-V1.0 , which surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks. For more details, please refer to WizardCoder.

Model	Checkpoint	Paper	HumanEval	MBPP	Demo	License
WizardCoder-15B-V1.0	🤗 HF Link	📃 [WizardCoder]	57.3	51.8		OpenRAIL-M

Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.

Model	Checkpoint	Paper	GSM8k	MATH	Online Demo	License
WizardMath-70B-V1.0	🤗 HF Link	📃 [WizardMath]	81.6	22.7	Demo	Llama 2
WizardMath-13B-V1.0	🤗 HF Link	📃 [WizardMath]	63.9	14.0	Demo	Llama 2
WizardMath-7B-V1.0	🤗 HF Link	📃 [WizardMath]	54.9	10.7	Demo	Llama 2

[08/09/2023] We released WizardLM-70B-V1.0 model. Here is Full Model Weight.

^Model	^Checkpoint	^Paper	^MT-Bench	^AlpacaEval	^GSM8k	^HumanEval	^License
^{WizardLM-70B-V1.0}	^{🤗 HF Link}	^{📃Coming Soon}	^7.78	^92.91%	^77.6%	^50.6	^{Llama 2 License}
^{WizardLM-13B-V1.2}	^{🤗 HF Link}		^7.06	^89.17%	^55.3%	^36.6	^{Llama 2 License}
^{WizardLM-13B-V1.1}	^{🤗 HF Link}		^6.76	^86.32%		^25.0	^{Non-commercial}
^{WizardLM-30B-V1.0}	^{🤗 HF Link}		^7.01			^37.8	^{Non-commercial}
^{WizardLM-13B-V1.0}	^{🤗 HF Link}		^6.35	^75.31%		^24.0	^{Non-commercial}
^{WizardLM-7B-V1.0}	^{🤗 HF Link}	^{📃 [WizardLM]}				^19.1	^{Non-commercial}