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 73.53846154
651 73.57910906
652 73.46625767
653 73.50689127
654 73.39449541
655 73.28244275
656 73.17073171
657 73.21156773
658 73.25227964
659 73.14112291
660 73.18181818
661 73.07110439
662 73.11178248
663 73.15233786
664 73.19277108
665 73.23308271
666 73.27327327
667 73.31334333
668 73.20359281
669 73.24364723
670 73.13432836
671 73.17436662
672 73.21428571
673 73.25408618
674 73.29376855
675 73.33333333
676 73.37278107
677 73.41211226
678 73.45132743
679 73.49042710
680 73.52941176
681 73.42143906
682 73.46041056
683 73.35285505
684 73.39181287
685 73.43065693
686 73.46938776
687 73.50800582
688 73.54651163
689 73.43976778
690 73.47826087
691 73.37192475
692 73.26589595
693 73.30447330
694 73.34293948
695 73.23741007
696 73.27586207
697 73.31420373
698 73.20916905
699 73.10443491
700 73.00000000
701 72.89586305
702 72.93447293
703 72.97297297
704 73.01136364
705 73.04964539
706 73.08781870
707 73.12588402
708 73.16384181
709 73.20169252
710 73.23943662
711 73.27707454
712 73.31460674
713 73.35203366
714 73.24929972
715 73.14685315
716 73.18435754
717 73.22175732
718 73.11977716
719 73.01808067
720 73.05555556
721 72.95423024
722 72.99168975
723 73.02904564
724 72.92817680
725 72.96551724
726 72.86501377
727 72.90233838
728 72.93956044
729 72.97668038
730 73.01369863
731 73.05061560
732 73.08743169
733 73.12414734
734 73.16076294
735 73.19727891
736 73.09782609
737 72.99864315
738 73.03523035
739 72.93640054
740 72.83783784
741 72.87449393
742 72.91105121
743 72.94751009
744 72.84946237
745 72.88590604
746 72.92225201
747 72.82463186
748 72.72727273
749 72.63017356
750 72.66666667
Final result: 72.6667 +/- 1.6284
Random chance: 25.0083 +/- 1.5824
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