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
- 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 new
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 new
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
| language: en | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen3-14B | |
| tags: | |
| - tool-calling | |
| - routing | |
| - code-generation | |
| - typescript | |
| - healthcare | |
| - aac | |
| - qwen3 | |
| - gguf | |
| # prism-coder:14b β Dual-Purpose: Tool Routing + Healthcare TypeScript Coder | |
| Fine-tuned Qwen3-14B for the [Prism AAC](https://github.com/dcostenco/prism-aac) / Synalux healthcare platform. | |
| Two trained capabilities in one model family: | |
| - **Routing** (v36): 6-tool routing for Prism MCP sessions β 100% BFCL | |
| - **Coding** (v42): Synalux-pattern TypeScript code generation β 22/22 checks (100%) | |
| --- | |
| ## Coding Eval β v42 (Current Production Coder) | |
| **22/22 (100%)** on the Synalux healthcare TypeScript eval. | |
| Task: write a production Next.js API route for X12 835 ERA reconciliation against existing 837P claims. | |
| | Check | Pass | | |
| |-------|------| | |
| | withAudit wrapper | β | | |
| | authenticateRequest | β | | |
| | supabaseAdmin (not client) | β | | |
| | cross-tenant guard (workspace_members + BILLING_ROLES) | β | | |
| | UUID_RX validation | β | | |
| | decryptPhi before PHI access | β | | |
| | HIPAA audit (hipaa_access_log) | β | | |
| | HIPAA non-blocking (.then) | β | | |
| | 409 already-reconciled guard | β | | |
| | 422 no CLP segments | β | | |
| | parse CLP segment | β | | |
| | parse SVC segment | β | | |
| | parse CAS CO (contractual) adjustment | β | | |
| | parse CAS PR (patient responsibility) | β | | |
| | GL cash_received entry | β | | |
| | GL contractual_adjustment entry | β | | |
| | GL patient_ar entry | β | | |
| | claim status map (1=paid) | β | | |
| | claim status map (4=denied) | β | | |
| | no postgres detail in 500 | β | | |
| | belt-and-suspenders workspace_id eq on update | β | | |
| | marks ERA file reconciled | β | | |
| Training chain: Qwen3-14B β v34 (1000-iter routing, 18/22) β v39 (HIPAA+CAS patch, 20/22) β v42 (claim status patch, 22/22). | |
| ### v42 Training Details | |
| - **Base**: Qwen/Qwen3-14B (BF16) | |
| - **Corpus**: v28 Synalux codebase SFT + targeted patch (claim status Γ 50 examples, resume from v39) | |
| - **Training**: MLX LoRA, rank=16, 8 layers, 100 iters, LR=5e-7 | |
| - **Final loss**: 0.036 (converged) | |
| - **Merge**: direct safetensors LoRA merge β GGUF F16 β Q4_K_M | |
| --- | |
| ## BFCL Routing Benchmark β v36 | |
| **Mean: 100.0% PERFECT** (3-seed average, seeds 2027/2028/2029, 102 cases each) | |
| | Category | Accuracy | | |
| |----------|:--------:| | |
| | aac (AAC phrase requests) | 100% | | |
| | cmpct (ledger compaction) | 100% | | |
| | edge (multi-step compound) | 100% | | |
| | hand (agent handoff) | 100% | | |
| | info (general facts) | 100% | | |
| | irrel (irrelevant/live queries) | 100% | | |
| | know (knowledge base search) | 100% | | |
| | load (session context loading) | 100% | | |
| | pred (factual queries) | 100% | | |
| | save (session ledger save) | 100% | | |
| | smem (session memory search) | 100% | | |
| | tran (translation) | 100% | | |
| ### Tools (routing model) | |
| | Tool | Trigger | | |
| |------|---------| | |
| | `session_load_context` | Load/resume project context | | |
| | `session_save_ledger` | Note/log/record/remember | | |
| | `session_save_handoff` | Pass state to next agent/session | | |
| | `session_compact_ledger` | Shrink/prune ledger | | |
| | `session_search_memory` | Recall prior session discussions | | |
| | `knowledge_search` | Search stored knowledge base | | |
| --- | |
| ## Version History | |
| | Version | Eval | Type | Notes | | |
| |---------|------|------|-------| | |
| | v42 | **22/22 coding (100%)** | Coder | Claim status patch on v39; zero tolerance policy | | |
| | v39 | 20/22 coding | Coder | HIPAA non-blocking + CAS CO/PR fixes | | |
| | v36 | **100% BFCL routing** | Router | smem boundary + hand trigger fixes | | |
| | v34 | 98.0% BFCL routing | Router | hand/save/smem fixes | | |
| | v33 | 97.1% BFCL routing | Router | irrel/tran/smem fixes | | |
| ## GGUF Files | |
| | File | Use | Size | | |
| |------|-----|------| | |
| | `qwen3-14b-v42-q4km.gguf` | **Coding** β production Synalux TypeScript | ~9 GB | | |
| | `prism-coder-14b-v36-q4km.gguf` | **Routing** β Prism MCP tool routing | ~9 GB | | |
| | `qwen3-14b-v34-q4km.gguf` | Routing (prior) | ~9 GB | | |
| ## Usage | |
| ```bash | |
| # Load as coding model | |
| ollama pull dcostenco/prism-coder-14b | |
| # Then use qwen3-14b-v42-q4km.gguf Modelfile | |
| # Load as routing model | |
| # Use prism-coder-14b-v36-q4km.gguf Modelfile | |
| ``` | |