Instructions to use dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach with Transformers:

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

pipe = pipeline("text-generation", model="dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach")
model = AutoModelForCausalLM.from_pretrained("dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach

SGLang

How to use dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach 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 "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach" \
    --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": "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach",
		"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 "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach" \
        --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": "dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach with Docker Model Runner:
```
docker model run hf.co/dddsaty/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach
```

dddsaty commited on Feb 14, 2024

Commit

8063c4a

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Update README.md

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@@ -19,6 +19,11 @@ pipeline_tag: text-generation
 **Adapter Corpus**
 - [We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs](https://huggingface.co/datasets/We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs)
 **Log**
 - 2024.02.13: Initial version Upload

 **Adapter Corpus**
 - [We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs](https://huggingface.co/datasets/We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs)
+**Score**
+|Average|ARC|HellaSwag|MMLU|TruthfulQA|Winogrande|GSM8K|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|76.09|73.89|88.94|65.03|71.24|87.61|69.83|
 **Log**
 - 2024.02.13: Initial version Upload