Instructions to use Local-Novel-LLM-project/Vecteus-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Local-Novel-LLM-project/Vecteus-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Local-Novel-LLM-project/Vecteus-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/Vecteus-v1")
model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/Vecteus-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Local-Novel-LLM-project/Vecteus-v1 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-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Local-Novel-LLM-project/Vecteus-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Local-Novel-LLM-project/Vecteus-v1

SGLang

How to use Local-Novel-LLM-project/Vecteus-v1 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-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Local-Novel-LLM-project/Vecteus-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Local-Novel-LLM-project/Vecteus-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Local-Novel-LLM-project/Vecteus-v1 with Docker Model Runner:
```
docker model run hf.co/Local-Novel-LLM-project/Vecteus-v1
```

umisetokikaze commited on May 1, 2024

Commit

6c81f4d

verified ·

1 Parent(s): 187bddc

Update README.md

Browse files

Files changed (1) hide show

README.md +13 -7

README.md CHANGED Viewed

@@ -56,17 +56,23 @@ We would like to take this opportunity to thank
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/Vecteus-v1", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/Vecteus-v1")
-prompt = "Once upon a time,"
-input_ids = tokenizer.encode(prompt, return_tensors="pt")
-output = model.generate(input_ids, max_length=100, do_sample=True)
-generated_text = tokenizer.decode(output)
-print(generated_text)
 ````
 ## Merge recipe

 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "Local-Novel-LLM-project/Vecteus-v1"
+new_tokens = 1024
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "
+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)
+print(tokenizer.batch_decode(generated_ids)[0])
 ````
 ## Merge recipe