Instructions to use InferenceIllusionist/Magic-Dolphin-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InferenceIllusionist/Magic-Dolphin-7b with Transformers:

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

pipe = pipeline("text-generation", model="InferenceIllusionist/Magic-Dolphin-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("InferenceIllusionist/Magic-Dolphin-7b")
model = AutoModelForCausalLM.from_pretrained("InferenceIllusionist/Magic-Dolphin-7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use InferenceIllusionist/Magic-Dolphin-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InferenceIllusionist/Magic-Dolphin-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InferenceIllusionist/Magic-Dolphin-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InferenceIllusionist/Magic-Dolphin-7b

SGLang

How to use InferenceIllusionist/Magic-Dolphin-7b 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 "InferenceIllusionist/Magic-Dolphin-7b" \
    --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": "InferenceIllusionist/Magic-Dolphin-7b",
		"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 "InferenceIllusionist/Magic-Dolphin-7b" \
        --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": "InferenceIllusionist/Magic-Dolphin-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InferenceIllusionist/Magic-Dolphin-7b with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/Magic-Dolphin-7b
```

InferenceIllusionist commited on Mar 4, 2024

Commit

1d63765

verified ·

1 Parent(s): af20319

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ A linear merge of:
 These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO.
 ### Benchmark Performance
-| Name | Avg. | ARC | HellaSwag | MMLU | TruthfulQA | Winograde | GSM8K |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
 | <b>Magic-Dolphin-7b</b> | <u><b>67.48</b></u> | 65.78 | 85.61 | 64.64 | 58.01 | <u><b>79.64</b></u> | <u><b>51.18</b></u> |
 | dolphin-2.6-mistral-7b-dpo-laser | 67.28 | 66.3 | 85.73 | 63.16 | 61.71 | 79.16 | 47.61 |

 These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO.
 ### Benchmark Performance
+| Name | Avg. | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
 | <b>Magic-Dolphin-7b</b> | <u><b>67.48</b></u> | 65.78 | 85.61 | 64.64 | 58.01 | <u><b>79.64</b></u> | <u><b>51.18</b></u> |
 | dolphin-2.6-mistral-7b-dpo-laser | 67.28 | 66.3 | 85.73 | 63.16 | 61.71 | 79.16 | 47.61 |