Instructions to use colable/LDCC-CCK-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use colable/LDCC-CCK-slerp with Transformers:

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

pipe = pipeline("text-generation", model="colable/LDCC-CCK-slerp")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("colable/LDCC-CCK-slerp")
model = AutoModelForCausalLM.from_pretrained("colable/LDCC-CCK-slerp", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use colable/LDCC-CCK-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/colable/LDCC-CCK-slerp

SGLang

How to use colable/LDCC-CCK-slerp 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 "colable/LDCC-CCK-slerp" \
    --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": "colable/LDCC-CCK-slerp",
		"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 "colable/LDCC-CCK-slerp" \
        --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": "colable/LDCC-CCK-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use colable/LDCC-CCK-slerp with Docker Model Runner:
```
docker model run hf.co/colable/LDCC-CCK-slerp
```

colable commited on Jan 23, 2024

Commit

7a5372d

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1 Parent(s): d5446ab

Update README.md

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@@ -5,7 +5,7 @@ base_model:
 tags:
 - mergekit
 - merge
 ---
 # ldcc-cck-mixed
@@ -20,4 +20,4 @@ This model was merged using the SLERP merge method.
 The following models were included in the merge:
 * [JaeyeonKang/CCK-v1.0.0-DPO](https://huggingface.co/JaeyeonKang/CCK-v1.0.0-DPO)
-* [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)

 tags:
 - mergekit
 - merge
+license: mit
 ---
 # ldcc-cck-mixed
 The following models were included in the merge:
 * [JaeyeonKang/CCK-v1.0.0-DPO](https://huggingface.co/JaeyeonKang/CCK-v1.0.0-DPO)
+* [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)