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: 12,675 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 817 tasks in prompt
multiple_choice_score: selecting 750 random tasks from 817 tasks available
multiple_choice_score: preparing task data...done
multiple_choice_score : calculating TruthfulQA score over 750 tasks.
task acc_norm
1 100.00000000
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3 33.33333333
4 25.00000000
5 20.00000000
6 16.66666667
7 14.28571429
8 25.00000000
9 22.22222222
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11 18.18181818
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13 30.76923077
14 28.57142857
15 33.33333333
16 31.25000000
17 29.41176471
18 27.77777778
19 26.31578947
20 25.00000000
21 28.57142857
22 27.27272727
23 30.43478261
24 33.33333333
25 32.00000000
26 30.76923077
27 29.62962963
28 28.57142857
29 31.03448276
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32 31.25000000
33 33.33333333
34 32.35294118
35 31.42857143
36 30.55555556
37 32.43243243
38 31.57894737
39 33.33333333
40 32.50000000
41 31.70731707
42 30.95238095
43 30.23255814
44 31.81818182
45 33.33333333
46 32.60869565
47 34.04255319
48 35.41666667
49 34.69387755
50 36.00000000
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52 36.53846154
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54 35.18518519
55 36.36363636
56 35.71428571
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500 32.20000000
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620 33.87096774
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623 33.86837881
624 33.81410256
625 33.76000000
626 33.70607029
627 33.81180223
628 33.91719745
629 34.02225755
630 33.96825397
631 34.07290016
632 34.01898734
633 34.12322275
634 34.22712934
635 34.17322835
636 34.11949686
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638 34.01253918
639 33.95931142
640 34.06250000
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648 34.10493827
649 34.05238829
650 34.15384615
651 34.25499232
652 34.35582822
653 34.45635528
654 34.40366972
655 34.35114504
656 34.45121951
657 34.55098935
658 34.49848024
659 34.44613050
660 34.39393939
661 34.34190620
662 34.29003021
663 34.38914027
664 34.33734940
665 34.28571429
666 34.23423423
667 34.18290855
668 34.13173653
669 34.08071749
670 34.17910448
671 34.12816692
672 34.07738095
673 34.02674591
674 33.97626113
675 34.07407407
676 34.17159763
677 34.12112260
678 34.07079646
679 34.02061856
680 34.11764706
681 34.21439060
682 34.16422287
683 34.11420205
684 34.21052632
685 34.16058394
686 34.11078717
687 34.06113537
688 34.15697674
689 34.25253991
690 34.20289855
691 34.29811867
692 34.39306358
693 34.34343434
694 34.29394813
695 34.38848921
696 34.48275862
697 34.43328551
698 34.38395415
699 34.33476395
700 34.42857143
701 34.37945792
702 34.33048433
703 34.28165007
704 34.23295455
705 34.18439716
706 34.13597734
707 34.22913720
708 34.18079096
709 34.13258110
710 34.08450704
711 34.17721519
712 34.12921348
713 34.08134642
714 34.17366947
715 34.12587413
716 34.07821229
717 34.17015342
718 34.12256267
719 34.21418637
720 34.16666667
721 34.11927878
722 34.07202216
723 34.02489627
724 33.97790055
725 33.93103448
726 34.02203857
727 33.97524072
728 33.92857143
729 34.01920439
730 34.10958904
731 34.06292750
732 34.15300546
733 34.10641201
734 34.05994550
735 34.01360544
736 33.96739130
737 34.05698779
738 34.01084011
739 34.10013532
740 34.05405405
741 34.14304993
742 34.09703504
743 34.05114401
744 34.13978495
745 34.09395973
746 34.04825737
747 34.13654618
748 34.09090909
749 34.17890521
750 34.26666667
Final result: 34.2667 +/- 1.7342
Random chance: 19.8992 +/- 1.4588
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