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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended")
model = AutoModelForCausalLM.from_pretrained("Pretergeek/OpenChat-3.5-0106_8.99B_40Layers-Appended", 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 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

2ee5c5b

verified ·

1 Parent(s): af2100c

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -13

README.md CHANGED Viewed

@@ -219,19 +219,6 @@ slices:
 merge_method: passthrough
 dtype: bfloat16
-```
-## Citation
-```
-@misc{wu2024llamaproprogressivellama,
-      title={LLaMA Pro: Progressive LLaMA with Block Expansion},
-      author={Chengyue Wu and Yukang Gan and Yixiao Ge and Zeyu Lu and Jiahao Wang and Ye Feng and Ying Shan and Ping Luo},
-      year={2024},
-      eprint={2401.02415},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2401.02415},
-}
 ```
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Pretergeek__OpenChat-3.5-0106_BlockExpansion-40Layers-End)
@@ -246,3 +233,15 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 |MuSR (0-shot)      |11.78|
 |MMLU-PRO (5-shot)  |25.44|

 merge_method: passthrough
 dtype: bfloat16
 ```
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Pretergeek__OpenChat-3.5-0106_BlockExpansion-40Layers-End)
 |MuSR (0-shot)      |11.78|
 |MMLU-PRO (5-shot)  |25.44|
+## Citation
+```
+@misc{wu2024llamaproprogressivellama,
+      title={LLaMA Pro: Progressive LLaMA with Block Expansion},
+      author={Chengyue Wu and Yukang Gan and Yixiao Ge and Zeyu Lu and Jiahao Wang and Ye Feng and Ying Shan and Ping Luo},
+      year={2024},
+      eprint={2401.02415},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2401.02415},
+}
+```