Instructions to use matchaaaaa/Honey-Yuzu-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matchaaaaa/Honey-Yuzu-13B with Transformers:

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

pipe = pipeline("text-generation", model="matchaaaaa/Honey-Yuzu-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("matchaaaaa/Honey-Yuzu-13B")
model = AutoModelForMultimodalLM.from_pretrained("matchaaaaa/Honey-Yuzu-13B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use matchaaaaa/Honey-Yuzu-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matchaaaaa/Honey-Yuzu-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matchaaaaa/Honey-Yuzu-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/matchaaaaa/Honey-Yuzu-13B

SGLang

How to use matchaaaaa/Honey-Yuzu-13B 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 "matchaaaaa/Honey-Yuzu-13B" \
    --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": "matchaaaaa/Honey-Yuzu-13B",
		"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 "matchaaaaa/Honey-Yuzu-13B" \
        --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": "matchaaaaa/Honey-Yuzu-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use matchaaaaa/Honey-Yuzu-13B with Docker Model Runner:
```
docker model run hf.co/matchaaaaa/Honey-Yuzu-13B
```

matchaaaaa commited on Sep 6, 2024

Commit

418f5ad

verified ·

1 Parent(s): 1377b62

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -2

README.md CHANGED Viewed

@@ -1,6 +1,9 @@
 ---
 base_model:
-- mistralai/Mistral-7B-v0.1
 library_name: transformers
 tags:
 - mergekit
@@ -60,7 +63,7 @@ The following models were included in the merge:
   * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
   * [Fimbulvetr-11B-v2.1-16K](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2.1-16K)
   * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-* [WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
 ### The Special Sauce

 ---
 base_model:
+- Chunky-Lemon-Cookie-11B
+- Fimbulvetr-11B-v2.1-16K
+- senseable/WestLake-7B-v2
+base_model_relation: merge
 library_name: transformers
 tags:
 - mergekit
   * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
   * [Fimbulvetr-11B-v2.1-16K](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2.1-16K)
   * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
 ### The Special Sauce