Instructions to use dcostenco/prism-coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dcostenco/prism-coder-14b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dcostenco/prism-coder-14b", filename="prism-aac-14b-q4km.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dcostenco/prism-coder-14b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dcostenco/prism-coder-14b # Run inference directly in the terminal: llama-cli -hf dcostenco/prism-coder-14b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dcostenco/prism-coder-14b # Run inference directly in the terminal: llama-cli -hf dcostenco/prism-coder-14b
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 dcostenco/prism-coder-14b # Run inference directly in the terminal: ./llama-cli -hf dcostenco/prism-coder-14b
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 dcostenco/prism-coder-14b # Run inference directly in the terminal: ./build/bin/llama-cli -hf dcostenco/prism-coder-14b
Use Docker
docker model run hf.co/dcostenco/prism-coder-14b
- LM Studio
- Jan
- Ollama
How to use dcostenco/prism-coder-14b with Ollama:
ollama run hf.co/dcostenco/prism-coder-14b
- Unsloth Studio
How to use dcostenco/prism-coder-14b 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 dcostenco/prism-coder-14b 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 dcostenco/prism-coder-14b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dcostenco/prism-coder-14b to start chatting
- Pi
How to use dcostenco/prism-coder-14b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf dcostenco/prism-coder-14b
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dcostenco/prism-coder-14b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use dcostenco/prism-coder-14b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf dcostenco/prism-coder-14b
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default dcostenco/prism-coder-14b
Run Hermes
hermes
- Docker Model Runner
How to use dcostenco/prism-coder-14b with Docker Model Runner:
docker model run hf.co/dcostenco/prism-coder-14b
- Lemonade
How to use dcostenco/prism-coder-14b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dcostenco/prism-coder-14b
Run and chat with the model
lemonade run user.prism-coder-14b-{{QUANT_TAG}}List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -39,6 +39,21 @@ All 12 categories at 100%. No remaining failures.
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Eval: MLX inference + thinking, temperature=0, 3-seed mean.
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Gate: ≥90% = deploy.
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## Version History
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| Version | BFCL | Notes |
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Eval: MLX inference + thinking, temperature=0, 3-seed mean.
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Gate: ≥90% = deploy.
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## Cascade Benchmark (May 2026)
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Full desktop cascade: **14b → 32b → Claude Opus** (102 cases × 3 seeds)
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| Metric | Result |
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|--------|--------|
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| Cascade accuracy | **100.0%** (mean, 3 seeds) |
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| Opus-solo etalon | 98.3% |
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| Δ vs Opus | **+1.7%** |
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| Traffic served by 14b | **99%** (101/102 cases avg) |
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| Traffic escalated to 32b | 1% (1/102 avg) |
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| Traffic reaching Opus API | **0%** |
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Fine-tuned cascade outperforms Claude Opus on `edge` (+16.7%) and `know` (+14.3%).
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## Version History
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| Version | BFCL | Notes |
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