Instructions to use heegyu/WizardVicuna-open-llama-3b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use heegyu/WizardVicuna-open-llama-3b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="heegyu/WizardVicuna-open-llama-3b-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2")
model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use heegyu/WizardVicuna-open-llama-3b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "heegyu/WizardVicuna-open-llama-3b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "heegyu/WizardVicuna-open-llama-3b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/heegyu/WizardVicuna-open-llama-3b-v2

SGLang

How to use heegyu/WizardVicuna-open-llama-3b-v2 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 "heegyu/WizardVicuna-open-llama-3b-v2" \
    --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": "heegyu/WizardVicuna-open-llama-3b-v2",
		"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 "heegyu/WizardVicuna-open-llama-3b-v2" \
        --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": "heegyu/WizardVicuna-open-llama-3b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use heegyu/WizardVicuna-open-llama-3b-v2 with Docker Model Runner:
```
docker model run hf.co/heegyu/WizardVicuna-open-llama-3b-v2
```

WizardVicuna-open-llama-3b-v2 / README.md

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Adding Evaluation Results

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	---
	datasets:
	- heegyu/wizard_vicuna_70k_v2
	license: apache-2.0
	---

	Hyperparameters
	- 3/8 epoch(3rd epoch checkpoing while 8epoch training)
	- 1e-4 -> 1e-5 with cosine lr decay
	- batch size 128
	- max sequence length 2048
	- AdamW(weigth decay=0.01, b1=0.9, b2=0.99, grad_clip=1.0)
	- no warmup
	- BF16
	- Base Model: [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2)

	```
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2")
	model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2")

	inputs = tokenizer(["Human: Hi, nice to meet you!\n\nAssistant: "], return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=16)
	print(tokenizer.batch_decode(outputs, skip_special_tokens=False))
	```

	output: `['Human: Hi, nice to meet you!\n\nAssistant: Hello. Great to meet you too. Well, how can I assist you today?<\|endoftext\|>']`
	# [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_heegyu__WizardVicuna-open-llama-3b-v2)

	\| Metric \| Value \|
	\|-----------------------\|---------------------------\|
	\| Avg. \| 34.11 \|
	\| ARC (25-shot) \| 37.71 \|
	\| HellaSwag (10-shot) \| 66.6 \|
	\| MMLU (5-shot) \| 27.23 \|
	\| TruthfulQA (0-shot) \| 36.8 \|
	\| Winogrande (5-shot) \| 63.3 \|
	\| GSM8K (5-shot) \| 0.99 \|
	\| DROP (3-shot) \| 6.12 \|