Instructions to use amazingvince/Not-WizardLM-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amazingvince/Not-WizardLM-2-7B with Transformers:

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

pipe = pipeline("text-generation", model="amazingvince/Not-WizardLM-2-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amazingvince/Not-WizardLM-2-7B")
model = AutoModelForCausalLM.from_pretrained("amazingvince/Not-WizardLM-2-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amazingvince/Not-WizardLM-2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amazingvince/Not-WizardLM-2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amazingvince/Not-WizardLM-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amazingvince/Not-WizardLM-2-7B

SGLang

How to use amazingvince/Not-WizardLM-2-7B 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 "amazingvince/Not-WizardLM-2-7B" \
    --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": "amazingvince/Not-WizardLM-2-7B",
		"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 "amazingvince/Not-WizardLM-2-7B" \
        --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": "amazingvince/Not-WizardLM-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amazingvince/Not-WizardLM-2-7B with Docker Model Runner:
```
docker model run hf.co/amazingvince/Not-WizardLM-2-7B
```

add link to colab example

by pszemraj - opened Apr 16, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

Files changed (1) hide show

README.md +8 -0

README.md CHANGED Viewed

@@ -2,6 +2,12 @@
 license: apache-2.0
 ---
 Included is code ripped from fastchat with the expected chat templating.
 Also wiz.pdf is a pdf of the github blog showing the apache 2 release.
@@ -9,6 +15,8 @@ Link to wayback machine included: https://web.archive.org/web/20240415221214/htt
 ```python
 import dataclasses
 from enum import auto, Enum

 license: apache-2.0
 ---
+# amazingvince/Not-WizardLM-2-7B
+<a href="https://colab.research.google.com/gist/pszemraj/d3d74ceab942722b49188606785e2bfd/not-wizardlm-2-7b-inference.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
 Included is code ripped from fastchat with the expected chat templating.
 Also wiz.pdf is a pdf of the github blog showing the apache 2 release.
+## example
 ```python
 import dataclasses
 from enum import auto, Enum