Instructions to use eaddario/Dolphin3.0-Mistral-24B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="eaddario/Dolphin3.0-Mistral-24B-GGUF", filename="Dolphin3.0-Mistral-24B-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eaddario/Dolphin3.0-Mistral-24B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "eaddario/Dolphin3.0-Mistral-24B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
- Ollama
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with Ollama:
ollama run hf.co/eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
- Unsloth Studio
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for eaddario/Dolphin3.0-Mistral-24B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for eaddario/Dolphin3.0-Mistral-24B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eaddario/Dolphin3.0-Mistral-24B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with Docker Model Runner:
docker model run hf.co/eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
- Lemonade
How to use eaddario/Dolphin3.0-Mistral-24B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eaddario/Dolphin3.0-Mistral-24B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Dolphin3.0-Mistral-24B-GGUF-Q4_K_M
List all available models
lemonade list
File size: 12,677 Bytes
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common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 750 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
multiple_choice_score: there are 869 tasks in prompt
multiple_choice_score: selecting 750 random tasks from 869 tasks available
multiple_choice_score: preparing task data...done
multiple_choice_score : calculating TruthfulQA score over 750 tasks.
task acc_norm
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650 73.07692308
651 73.11827957
652 73.00613497
653 73.04747320
654 72.93577982
655 72.82442748
656 72.71341463
657 72.75494673
658 72.79635258
659 72.68588771
660 72.72727273
661 72.61724660
662 72.65861027
663 72.69984917
664 72.74096386
665 72.78195489
666 72.82282282
667 72.86356822
668 72.75449102
669 72.79521674
670 72.68656716
671 72.72727273
672 72.76785714
673 72.80832095
674 72.84866469
675 72.88888889
676 72.92899408
677 72.96898080
678 73.00884956
679 72.90132548
680 72.94117647
681 72.98091043
682 73.02052786
683 72.91361640
684 72.95321637
685 72.99270073
686 73.03206997
687 73.07132460
688 73.11046512
689 73.00435414
690 73.04347826
691 73.08248915
692 72.97687861
693 73.01587302
694 73.05475504
695 72.94964029
696 72.98850575
697 72.88378766
698 72.77936963
699 72.81831187
700 72.71428571
701 72.61055635
702 72.64957265
703 72.68847795
704 72.72727273
705 72.76595745
706 72.80453258
707 72.84299859
708 72.88135593
709 72.91960508
710 72.95774648
711 72.99578059
712 73.03370787
713 73.07152875
714 72.96918768
715 72.86713287
716 72.90502793
717 72.94281729
718 72.84122563
719 72.73991655
720 72.77777778
721 72.67683773
722 72.71468144
723 72.75242047
724 72.65193370
725 72.68965517
726 72.58953168
727 72.62723521
728 72.66483516
729 72.70233196
730 72.73972603
731 72.77701778
732 72.81420765
733 72.85129604
734 72.88828338
735 72.92517007
736 72.96195652
737 72.86295794
738 72.89972900
739 72.80108254
740 72.70270270
741 72.73954116
742 72.77628032
743 72.81292059
744 72.71505376
745 72.75167785
746 72.78820375
747 72.69076305
748 72.59358289
749 72.49666222
750 72.53333333
Final result: 72.5333 +/- 1.6309
Random chance: 25.0083 +/- 1.5824
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