Instructions to use Dans-DiscountModels/Dans-AdventurousWinds-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dans-DiscountModels/Dans-AdventurousWinds-7b with Transformers:

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

pipe = pipeline("text-generation", model="Dans-DiscountModels/Dans-AdventurousWinds-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/Dans-AdventurousWinds-7b")
model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/Dans-AdventurousWinds-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Dans-DiscountModels/Dans-AdventurousWinds-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dans-DiscountModels/Dans-AdventurousWinds-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dans-DiscountModels/Dans-AdventurousWinds-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dans-DiscountModels/Dans-AdventurousWinds-7b

SGLang

How to use Dans-DiscountModels/Dans-AdventurousWinds-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 "Dans-DiscountModels/Dans-AdventurousWinds-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": "Dans-DiscountModels/Dans-AdventurousWinds-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 "Dans-DiscountModels/Dans-AdventurousWinds-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": "Dans-DiscountModels/Dans-AdventurousWinds-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dans-DiscountModels/Dans-AdventurousWinds-7b with Docker Model Runner:
```
docker model run hf.co/Dans-DiscountModels/Dans-AdventurousWinds-7b
```

Difference from MK2

by mrfakename - opened Nov 23, 2023

Discussion

mrfakename

Nov 23, 2023

Hi, how is this different from the mk2 version?

PocketDoc

Dan's Discount Model Emporium org Nov 23, 2023

This version was trained on longer sequences (16384 tokens vs. 4192 tokens), in addition to this I processed the individual stories in the datasets into 16k token sequences whereas for mk1 they were left plain resulting in them being trimmed.

PocketDoc changed discussion status to closed Nov 23, 2023

PocketDoc changed discussion status to open Nov 23, 2023

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