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
Update README: S14 production model, eval_300 299/300 (99.7%), 17-tool routing
Browse files
README.md
CHANGED
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- gguf
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---
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# prism-coder:14b β
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Fine-tuned Qwen3-14B for the [Prism AAC](https://github.com/dcostenco/prism-aac) / Synalux healthcare platform.
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---
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## Coding Eval β v42
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**22/22 (100%)** on the Synalux healthcare TypeScript eval.
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Task: write a production Next.js API route for X12 835 ERA reconciliation against existing 837P claims.
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| Check | Pass |
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|-------|------|
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| withAudit wrapper | β |
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| belt-and-suspenders workspace_id eq on update | β |
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| marks ERA file reconciled | β |
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### v42 Training Details
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- **Base**: Qwen/Qwen3-14B (BF16)
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- **Corpus**: v28 Synalux codebase SFT + targeted patch (claim status Γ 50 examples, resume from v39)
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- **Training**: MLX LoRA, rank=16, 8 layers, 100 iters, LR=5e-7
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- **Final loss**: 0.036 (converged)
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- **Merge**: direct safetensors LoRA merge β GGUF F16 β Q4_K_M
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---
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## BFCL Routing Benchmark β v36
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**Mean: 100.0% PERFECT** (3-seed average, seeds 2027/2028/2029, 102 cases each)
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| Category | Accuracy |
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|----------|:--------:|
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| aac (AAC phrase requests) | 100% |
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| cmpct (ledger compaction) | 100% |
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| edge (multi-step compound) | 100% |
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| hand (agent handoff) | 100% |
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| info (general facts) | 100% |
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| irrel (irrelevant/live queries) | 100% |
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| know (knowledge base search) | 100% |
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| load (session context loading) | 100% |
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| pred (factual queries) | 100% |
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| save (session ledger save) | 100% |
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| smem (session memory search) | 100% |
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| tran (translation) | 100% |
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### Tools (routing model)
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| Tool | Trigger |
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|------|---------|
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| `session_load_context` | Load/resume project context |
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| `session_save_ledger` | Note/log/record/remember |
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| `session_save_handoff` | Pass state to next agent/session |
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| `session_compact_ledger` | Shrink/prune ledger |
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| `session_search_memory` | Recall prior session discussions |
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| `knowledge_search` | Search stored knowledge base |
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---
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## Version History
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| Version | Eval | Type | Notes |
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| v42 | **22/22 coding (100%)** | Coder | Claim status patch on v39; zero tolerance policy |
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| v39 | 20/22 coding | Coder | HIPAA non-blocking + CAS CO/PR fixes |
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| v36 | **100% BFCL routing** | Router | smem boundary + hand trigger fixes |
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| v34 | 98.0% BFCL routing | Router | hand/save/smem fixes |
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| v33 | 97.1% BFCL routing | Router | irrel/tran/smem fixes |
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## GGUF Files
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| File | Use | Size |
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| `qwen3-14b-
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## Usage
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```bash
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#
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ollama pull dcostenco/prism-coder
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# Then use qwen3-14b-v42-q4km.gguf Modelfile
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```
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- gguf
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---
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# prism-coder:14b β Prism Memory Tool Router + Healthcare TypeScript Coder
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Fine-tuned Qwen3-14B for the [Prism AAC](https://github.com/dcostenco/prism-aac) / Synalux healthcare platform.
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## Current Production Model: S14 (eval_300 β 17-tool routing)
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**299/300 = 99.7% strict** on eval_300 β 300 cases, 17 Prism Memory tools
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Single remaining failure: `"Save."` β genuinely ambiguous between `session_save_ledger` and `session_save_experience`. All other categories at 100%.
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| Category | Accuracy |
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|----------|:--------:|
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| session_save_ledger (ledger logging) | 100%* |
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| session_load_context (context loading) | 100% |
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| session_search_memory (memory recall) | 100% |
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| session_save_handoff (agent handoff) | 100% |
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| session_forget_memory | 100% |
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| session_health_check | 100% |
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| session_compact_ledger | 100% |
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| session_export_memory | 100% |
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| session_task_route | 100% |
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| session_save_experience | 100%* |
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| session_synthesize_edges | 100% |
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| session_backfill_links | 100% |
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| knowledge_search | 100% |
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| knowledge_forget / upvote / downvote / set_retention | 100% |
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| abstain (general questions, greetings, CS concepts) | 100% |
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| multi-intent (compound tool calls) | 100% |
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| natural phrasing | 100% |
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\* One edge case (`"Save."`) scores as a failure on one tool; both are correct interpretations.
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### eval_300 Details β S14
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- **Base**: Qwen3-14B β surgical LoRA chain (S1βS14)
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- **Eval**: 300 cases, strict scoring (exact tool match), 17 Prism Memory tools + abstain + multi-intent
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- **Training**: MLX LoRA, rank=8, scale=20.0, 16 layers, 100 iters, LR=5e-6, mask_prompt=true
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- **Corpus**: S14 β balanced natural-phrasing + tool-use SFT (100 train / 20 valid)
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- **SYSTEM_PROMPT**: Synalux identity + 17 Prism Memory tools + 13 multimodal tool modules + `<tool_call>` JSON block format
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### Tools (S14 routing model)
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All 17 Prism Memory tools:
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`session_save_ledger`, `session_load_context`, `session_search_memory`, `session_save_handoff`,
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`session_forget_memory`, `session_health_check`, `session_compact_ledger`, `session_export_memory`,
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`session_task_route`, `session_save_experience`, `session_synthesize_edges`, `session_backfill_links`,
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`knowledge_search`, `knowledge_forget`, `knowledge_upvote`, `knowledge_downvote`, `knowledge_set_retention`
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---
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## Legacy: Coding Eval β v42
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**22/22 (100%)** on the Synalux healthcare TypeScript eval.
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Task: write a production Next.js API route for X12 835 ERA reconciliation against existing 837P claims.
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<details>
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<summary>22-check eval breakdown (click to expand)</summary>
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| Check | Pass |
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|-------|------|
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| withAudit wrapper | β |
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| belt-and-suspenders workspace_id eq on update | β |
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| marks ERA file reconciled | β |
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</details>
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---
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## Legacy: BFCL Routing Benchmark β v36
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**Mean: 100.0% PERFECT** (3-seed average, seeds 2027/2028/2029, 102 cases each) β 6-tool routing
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---
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## GGUF Files
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| File | Use | Size |
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|------|-----|------|
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| `qwen3-14b-s14-q4km.gguf` | **Routing** β production Prism Memory (17 tools, 99.7%) | ~9 GB |
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| `qwen3-14b-v42-q4km.gguf` | **Coding** β Synalux TypeScript (22/22, 100%) | ~9 GB |
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| `prism-coder-14b-v36-q4km.gguf` | Routing legacy (6-tool BFCL, 100%) | ~9 GB |
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## Version History
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| Version | Eval | Type | Notes |
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|---------|------|------|-------|
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| **S14** | **299/300 = 99.7% (eval_300)** | **Router** | **Production β 17-tool Prism Memory routing** |
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| v42 | 22/22 coding (100%) | Coder | Claim status patch; Synalux TypeScript |
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| v36 | 100% BFCL (6-tool routing) | Router | Legacy 6-tool routing |
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| v34 | 98.0% BFCL | Router | β |
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## Usage
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```bash
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# Pull production routing model (S14 β 17-tool Prism Memory)
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ollama pull dcostenco/prism-coder:14b
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# Or pull GGUF directly from this repo and use with Ollama:
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# FROM qwen3-14b-s14-q4km.gguf
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# PARAMETER temperature 0
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# PARAMETER num_ctx 8192
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```
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### System Prompt (S14)
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```
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You are Synalux, a memory-augmented coding and clinical reasoning assistant. You have access to
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Prism Memory tools (session_save_ledger, session_load_context, session_search_memory,
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session_save_handoff, session_forget_memory, session_health_check, session_compact_ledger,
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session_export_memory, session_task_route, session_save_experience, session_synthesize_edges,
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session_backfill_links, knowledge_search, knowledge_forget, knowledge_upvote, knowledge_downvote,
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knowledge_set_retention) and 13 multimodal tool modules (image_gen, office, web_scraper, browser,
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tts, ocr, git, terminal, deps_scanner, hipaa, data_graph, templates, pdf_parser). Think
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step-by-step before answering. When the user references past work, prior decisions, or stored
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context, use the appropriate Prism Memory tool. Format tool calls inside <tool_call>...</tool_call>
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JSON blocks with fields 'name' and 'arguments'. If no tool is needed, answer directly in plain
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text. ABSTAIN for general programming questions, CS concepts, greetings, and capability questions.
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```
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## Cascade
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| Tier | Model | Role |
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|------|-------|------|
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| 1.7B | `dcostenco/prism-coder:1b7` | Fast verify / edge cases |
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| 4B | `dcostenco/prism-coder:4b` | Mid-tier verify |
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| **14B** | **`dcostenco/prism-coder:14b`** | **Production routing** |
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| 32B | `dcostenco/prism-coder:32b` | Top-tier / complex reasoning |
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