Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
code
Eval Results (legacy)
text-generation-inference
Instructions to use InferenceIllusionist/Magic-Dolphin-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("InferenceIllusionist/Magic-Dolphin-7b") model = AutoModelForCausalLM.from_pretrained("InferenceIllusionist/Magic-Dolphin-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
-
Update README.md
Verified signature7f7aed9
-
Update README.md
Verified signaturebd0d487
-
Update README.md
Verified signatureUpdated merlinite-7b metrics from Open LLM leaderboardf318713
-
Upload asc-ii.jpg
Verified signature13c4766
-
adding base_model
Verified signature48b6b36 -
Adding Evaluation Results (#1)
Verified signature- Adding Evaluation Results (f3fa6fb9f805af86ea7d37dc2db5a337a0e2c120) Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>cb3bccb -
Update README.md
Verified signature1d63765 -
Update README.md
Verified signatureaf20319 -
Update README.md
Verified signatureeecd959 -
Added open llm benchmark results table.
Verified signature3aebbd1
-
Update README.md
Verified signature3ec4751 -
Update README.md
Verified signature4cd26c6 -
Update README.md
Verified signature7712bb0 -
Update README.md
Verified signature60a21a0 -
Upload magic-dolphin.JPG
Verified signatureefcec60 -
Update README.md
Verified signature590f041 -
Upload magic-dolphin.jfif
Verified signature4b08ea6 -
Update README.md
Verified signaturebf1884d -
Upload 10 files
Verified signature7338970 -
initial commit
Verified signature206b800