Instructions to use Local-Novel-LLM-project/Vecteus-Constant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Local-Novel-LLM-project/Vecteus-Constant with Transformers:

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

pipe = pipeline("text-generation", model="Local-Novel-LLM-project/Vecteus-Constant")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/Vecteus-Constant")
model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/Vecteus-Constant", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Local-Novel-LLM-project/Vecteus-Constant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Local-Novel-LLM-project/Vecteus-Constant

SGLang

How to use Local-Novel-LLM-project/Vecteus-Constant 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 "Local-Novel-LLM-project/Vecteus-Constant" \
    --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": "Local-Novel-LLM-project/Vecteus-Constant",
		"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 "Local-Novel-LLM-project/Vecteus-Constant" \
        --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": "Local-Novel-LLM-project/Vecteus-Constant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Local-Novel-LLM-project/Vecteus-Constant with Docker Model Runner:
```
docker model run hf.co/Local-Novel-LLM-project/Vecteus-Constant
```

umisetokikaze commited on May 4, 2024

Commit

51b1422

verified ·

1 Parent(s): 647e4a1

Update README.md

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@@ -72,7 +72,7 @@ system_prompt = "あなたはプロの小説家です。\n小説を書いてく
 prompt = input("Enter a prompt: ")
 system_prompt += prompt + "\n-------- "
-model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)

 prompt = input("Enter a prompt: ")
 system_prompt += prompt + "\n-------- "
+model_inputs = tokenizer([system_prompt], return_tensors="pt").to("cuda")
 generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)