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 Jul 15, 2024

Commit

bb3150b

verified ·

1 Parent(s): 6348703

Update README.md

Browse files

Files changed (1) hide show

README.md +42 -42

README.md CHANGED Viewed

@@ -1,42 +1,42 @@
----
-base_model: []
-library_name: transformers
-tags:
-- mergekit
-- merge
----
-# Lady-Grey-13B
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the passthrough merge method.
-### Models Merged
-The following models were included in the merge:
-* mega\splice
-* D:/MLnonsense/models/senseable_WestLake-7B-v2
-* mega\Chunky-Lemon-Cookie-11B
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-dtype: float32
-merge_method: passthrough
-slices:
-- sources:
-  - layer_range: [0, 16]
-    model: D:/MLnonsense/models/senseable_WestLake-7B-v2
-- sources:
-  - layer_range: [0, 8]
-    model: mega\splice
-- sources:
-  - layer_range: [16, 48]
-    model: mega\Chunky-Lemon-Cookie-11B
-```

+---
+base_model: []
+library_name: transformers
+tags:
+- mergekit
+- merge
+---
+# Honey-Yuzu-13B
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the passthrough merge method.
+### Models Merged
+The following models were included in the merge:
+* mega\splice
+* D:/MLnonsense/models/senseable_WestLake-7B-v2
+* mega\Chunky-Lemon-Cookie-11B
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+dtype: float32
+merge_method: passthrough
+slices:
+- sources:
+  - layer_range: [0, 16]
+    model: D:/MLnonsense/models/senseable_WestLake-7B-v2
+- sources:
+  - layer_range: [0, 8]
+    model: mega\splice
+- sources:
+  - layer_range: [16, 48]
+    model: mega\Chunky-Lemon-Cookie-11B
+```