Instructions to use Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended with Transformers:

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

pipe = pipeline("text-generation", model="Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended")
model = AutoModelForMultimodalLM.from_pretrained("Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended")
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 Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended

SGLang

How to use Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended 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 "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended" \
    --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": "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended",
		"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 "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended" \
        --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": "Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended with Docker Model Runner:
```
docker model run hf.co/Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended
```

Pretergeek commited on Jul 31, 2024

Commit

63b3d84

verified ·

1 Parent(s): 2ee5c5b

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -120,7 +120,7 @@ The authors of the paper added new layers interleaved in between the original la
 I used the same method but added the new layers to the end of the model. My rationale is that the level of abstraction increases with each layer of the model. So, while new layers spread along the original layers will help the model to learn new tasks, adding layers to the end of the model and then re-training/fine-tuning the model on tasks it already performs well could improve the models understanding of those task and perform them better by employing more complex reasoning.
-This model has not yet received additional training, so it should perform close to the original model. Evaluations are pending and will be added when available.
 ### Models Merged

 I used the same method but added the new layers to the end of the model. My rationale is that the level of abstraction increases with each layer of the model. So, while new layers spread along the original layers will help the model to learn new tasks, adding layers to the end of the model and then re-training/fine-tuning the model on tasks it already performs well could improve the models understanding of those task and perform them better by employing more complex reasoning.
+This model has not yet received additional training, so it should perform close to the original model.
 ### Models Merged