Text Generation
Transformers
Safetensors
English
mixtral
Generated from Trainer
axolotl
conversational
text-generation-inference
Instructions to use dphn/dolphin-2.9-mixtral-8x22b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dphn/dolphin-2.9-mixtral-8x22b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dphn/dolphin-2.9-mixtral-8x22b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9-mixtral-8x22b") model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9-mixtral-8x22b", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dphn/dolphin-2.9-mixtral-8x22b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dphn/dolphin-2.9-mixtral-8x22b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dphn/dolphin-2.9-mixtral-8x22b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dphn/dolphin-2.9-mixtral-8x22b
- SGLang
How to use dphn/dolphin-2.9-mixtral-8x22b 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 "dphn/dolphin-2.9-mixtral-8x22b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dphn/dolphin-2.9-mixtral-8x22b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "dphn/dolphin-2.9-mixtral-8x22b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dphn/dolphin-2.9-mixtral-8x22b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dphn/dolphin-2.9-mixtral-8x22b with Docker Model Runner:
docker model run hf.co/dphn/dolphin-2.9-mixtral-8x22b
Update README.md
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by dagbs - opened
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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My appreciation for the sponsors of Dolphin 2.9:
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
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My appreciation for the sponsors of Dolphin 2.9:
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
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