Instructions to use dmayhem93/RandomWalkADC_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dmayhem93/RandomWalkADC_v0 with Transformers:

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

pipe = pipeline("text-generation", model="dmayhem93/RandomWalkADC_v0")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dmayhem93/RandomWalkADC_v0")
model = AutoModelForCausalLM.from_pretrained("dmayhem93/RandomWalkADC_v0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dmayhem93/RandomWalkADC_v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dmayhem93/RandomWalkADC_v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dmayhem93/RandomWalkADC_v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dmayhem93/RandomWalkADC_v0

SGLang

How to use dmayhem93/RandomWalkADC_v0 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 "dmayhem93/RandomWalkADC_v0" \
    --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": "dmayhem93/RandomWalkADC_v0",
		"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 "dmayhem93/RandomWalkADC_v0" \
        --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": "dmayhem93/RandomWalkADC_v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dmayhem93/RandomWalkADC_v0 with Docker Model Runner:
```
docker model run hf.co/dmayhem93/RandomWalkADC_v0
```

dmayhem93 commited on Mar 9, 2023

Commit

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1 Parent(s): bddb62a

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+# Model Card for RandomWalkADC_v0
+Based on GPT-J, this model attemps to model a conversation by rejecting utterances to switch to a different reply.
+Reddit comment chains are used to model this, with the first instance being the highest scoring reply to the current comment,
+which we will randomly reject and go to the next highest comment. This repeats until we either run out of comments, or accept the current comment.
+We sample the reply chain in this manner until we do not have any more replies to the current comment.
+The input to the model will look something like this:
+```ExampleUser
+Example Title
+Example Comment <BACKGROUND_INDEX_TOKEN> Other_User
+Edit: Second <REJECTED_UTTERANCE> First_User
+First! <SELECTED_UTTERANCE> Other_User
+😭 <SELECTED_UTTERANCE>
+```