Instructions to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="LoupGarou/WizardCoder-Guanaco-15B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoupGarou/WizardCoder-Guanaco-15B-V1.0")
model = AutoModelForCausalLM.from_pretrained("LoupGarou/WizardCoder-Guanaco-15B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoupGarou/WizardCoder-Guanaco-15B-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": "LoupGarou/WizardCoder-Guanaco-15B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoupGarou/WizardCoder-Guanaco-15B-V1.0

SGLang

How to use LoupGarou/WizardCoder-Guanaco-15B-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 "LoupGarou/WizardCoder-Guanaco-15B-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": "LoupGarou/WizardCoder-Guanaco-15B-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 "LoupGarou/WizardCoder-Guanaco-15B-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": "LoupGarou/WizardCoder-Guanaco-15B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with Docker Model Runner:
```
docker model run hf.co/LoupGarou/WizardCoder-Guanaco-15B-V1.0
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Nov 17, 2023

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

+14

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README.md +14 -0

README.md CHANGED Viewed

@@ -78,3 +78,17 @@ This model, WizardCoder-Guanaco-15B-V1.0, is simply building on the efforts of t
 A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.
 Moreover, a special note of thanks to the [Hugging Face](https://huggingface.co/) team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated.

 A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.
 Moreover, a special note of thanks to the [Hugging Face](https://huggingface.co/) team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_LoupGarou__WizardCoder-Guanaco-15B-V1.0)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 30.36   |
+| ARC (25-shot)         | 30.46          |
+| HellaSwag (10-shot)   | 45.59    |
+| MMLU (5-shot)         | 26.79         |
+| TruthfulQA (0-shot)   | 46.39   |
+| Winogrande (5-shot)   | 53.12   |
+| GSM8K (5-shot)        | 1.44        |
+| DROP (3-shot)         | 8.71         |