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wahidmounir
/
fugusashi-v1.3-Q8_0-GGUF

HERMES
GGUF
English
llm-routing
model-chooser
intelligent-orchestration
modernbert
fine-tuned
cma-es
federated-learning
cost-optimization
open-source
production-ai
model-dispatch
prompt-routing
multi-model
ensemble-decision
human-readable-decisions
federated-llm
evolution-strategy
ai-infrastructure
llm-inference
prompt-classification
model-selection
intelligent-dispatch
cost-effective-llm
production-routing
free-llm-api
openrouter
transformer-based
classifier
v1.3.0
fugusashi
eulogik
self-hosted
on-device
cpu-inference
low-latency
83ms-inference
3-model-classes
gpt-oss
lfm
149m-params
cross-entropy-training
cosine-scheduler
differential-privacy
federated-averaging
multi-agent
task-orchestration
grpo-reinforcement-learning
human-in-the-loop
explainable-ai
xai
interpretable-ai
natural-language-explanation
confidence-scoring
fallback-strategy
ensemble-ai
mlops
ai-orchestration
llmops
prompt-engineering
token-efficiency
aiops
llm-observability
model-governance
open-ai-compatibility
chat-completion-api
openai-compatible
v1-chat-completions
streaming
function-calling
agentic-ai
rag-routing
enterprise-llm
startup-llm
healthcare-llm
code-assistant
creative-writing-ai
math-reasoning
factual-qna
llm-gateway
smart-proxy
model-proxy
ai-gateway
inference-optimization
gpu-saving
cost-saving
latency-optimization
real-time-inference
edge-ai
on-device-inference
embedded-llm
edge-deployment
mobile-friendly
cpu-only
no-gpu-needed
feature-extraction
Model card Files Files and versions
xet
Community

Instructions to use wahidmounir/fugusashi-v1.3-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • HERMES

    How to use wahidmounir/fugusashi-v1.3-Q8_0-GGUF with HERMES:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use wahidmounir/fugusashi-v1.3-Q8_0-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 wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    # Run inference directly in the terminal:
    llama cli -hf wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    # Run inference directly in the terminal:
    llama cli -hf wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    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 wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    # Run inference directly in the terminal:
    ./llama-cli -hf wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    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 wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    Use Docker
    docker model run hf.co/wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
  • LM Studio
  • Jan
  • Ollama

    How to use wahidmounir/fugusashi-v1.3-Q8_0-GGUF with Ollama:

    ollama run hf.co/wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
  • Unsloth Studio

    How to use wahidmounir/fugusashi-v1.3-Q8_0-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 wahidmounir/fugusashi-v1.3-Q8_0-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 wahidmounir/fugusashi-v1.3-Q8_0-GGUF to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for wahidmounir/fugusashi-v1.3-Q8_0-GGUF to start chatting
  • Docker Model Runner

    How to use wahidmounir/fugusashi-v1.3-Q8_0-GGUF with Docker Model Runner:

    docker model run hf.co/wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
  • Lemonade

    How to use wahidmounir/fugusashi-v1.3-Q8_0-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull wahidmounir/fugusashi-v1.3-Q8_0-GGUF:Q8_0
    Run and chat with the model
    lemonade run user.fugusashi-v1.3-Q8_0-GGUF-Q8_0
    List all available models
    lemonade list
  • Atomic Chat
fugusashi-v1.3-Q8_0-GGUF
161 MB
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  • 1 contributor
History: 5 commits
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wahidmounir
Update README.md
e19d411 verified about 12 hours ago
  • .gitattributes
    1.58 kB
    Upload fugusashi-v1.3-q8_0.gguf with huggingface_hub 2 days ago
  • README.md
    20.7 kB
    Update README.md about 12 hours ago
  • fugusashi-v1.3-q8_0.gguf
    161 MB
    xet
    Upload fugusashi-v1.3-q8_0.gguf with huggingface_hub 2 days ago