Instructions to use VitalContribution/Evangelion-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VitalContribution/Evangelion-7B with Transformers:

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

pipe = pipeline("text-generation", model="VitalContribution/Evangelion-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VitalContribution/Evangelion-7B")
model = AutoModelForCausalLM.from_pretrained("VitalContribution/Evangelion-7B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VitalContribution/Evangelion-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VitalContribution/Evangelion-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VitalContribution/Evangelion-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VitalContribution/Evangelion-7B

SGLang

How to use VitalContribution/Evangelion-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 "VitalContribution/Evangelion-7B" \
    --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": "VitalContribution/Evangelion-7B",
		"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 "VitalContribution/Evangelion-7B" \
        --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": "VitalContribution/Evangelion-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VitalContribution/Evangelion-7B with Docker Model Runner:
```
docker model run hf.co/VitalContribution/Evangelion-7B
```

VitalContribution commited on Jan 12, 2024

Commit

826333a

verified ·

1 Parent(s): dcbd7ca

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -12,18 +12,17 @@ library_name: adapter-transformers
 I was just curious to see if something special might happen if one uses:
 $$
-\text{{Evangelion}} = \text{{high-quality DPO dataset}} + \text{{merge of DPO optimized model and non-DPO optimized model}}
 $$
-The underlying model that I used was `/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp`.
 # Dataset
 Dataset: `/argilla/distilabel-intel-orca-dpo-pairs`
-The dataset was quality over quantity roughly ~3000 samples but they were high quality (aqccording to the chosen_score).
 The following filters were applied to the original dataset:
 ```python
 dataset = dataset.filter(
@@ -35,7 +34,8 @@ dataset = dataset.filter(
 ```
 # Chat Template
-I decided to go with the ChatML template which I also integrated into this models tokenizer.
 ```
 <|im_start|>system
 {system}<|im_end|>

 I was just curious to see if something special might happen if one uses:
 $$
+\text{{Evangelion}} = \text{{high-quality DPO dataset}} + \text{{merge of DPO optimized and non-DPO optimized model}}
 $$
+The underlying model that I used was `/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp`.
+**Disclaimer:** I'm by no means a pro; therefore, no guarantee. I just wanted to put a cool anime picture on my model card.
 # Dataset
 Dataset: `/argilla/distilabel-intel-orca-dpo-pairs`
+The dataset was quality over quantity roughly ~3000 samples but they were high quality (aqccording to the chosen_score).
 The following filters were applied to the original dataset:
 ```python
 dataset = dataset.filter(
 ```
 # Chat Template
+I decided to go with the ChatML which is used for OpenHermes2.5
+By the way I integreated the chat template into the models tokenizer.
 ```
 <|im_start|>system
 {system}<|im_end|>