Instructions to use dcostenco/prism-coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

prism-coder:14b — Prism Memory Tool Router + Healthcare TypeScript Coder

Fine-tuned Qwen3-14B for the Prism AAC / Synalux healthcare platform.

Current Production Model: S14 (eval_300 — 17-tool routing)

299/300 = 99.7% strict on eval_300 — 300 cases, 17 Prism Memory tools

Single remaining failure: "Save." — genuinely ambiguous between session_save_ledger and session_save_experience. All other categories at 100%.

Category	Accuracy
session_save_ledger (ledger logging)	100%*
session_load_context (context loading)	100%
session_search_memory (memory recall)	100%
session_save_handoff (agent handoff)	100%
session_forget_memory	100%
session_health_check	100%
session_compact_ledger	100%
session_export_memory	100%
session_task_route	100%
session_save_experience	100%*
session_synthesize_edges	100%
session_backfill_links	100%
knowledge_search	100%
knowledge_forget / upvote / downvote / set_retention	100%
abstain (general questions, greetings, CS concepts)	100%
multi-intent (compound tool calls)	100%
natural phrasing	100%

* One edge case ("Save.") scores as a failure on one tool; both are correct interpretations.

eval_300 Details — S14

Base: Qwen3-14B → surgical LoRA chain (S1→S14)
Eval: 300 cases, strict scoring (exact tool match), 17 Prism Memory tools + abstain + multi-intent
Training: MLX LoRA, rank=8, scale=20.0, 16 layers, 100 iters, LR=5e-6, mask_prompt=true
Corpus: S14 — balanced natural-phrasing + tool-use SFT (100 train / 20 valid)
SYSTEM_PROMPT: Synalux identity + 17 Prism Memory tools + 13 multimodal tool modules + <tool_call> JSON block format

Tools (S14 routing model)

All 17 Prism Memory tools: session_save_ledger, session_load_context, session_search_memory, session_save_handoff, session_forget_memory, session_health_check, session_compact_ledger, session_export_memory, session_task_route, session_save_experience, session_synthesize_edges, session_backfill_links, knowledge_search, knowledge_forget, knowledge_upvote, knowledge_downvote, knowledge_set_retention

Legacy: Coding Eval — v42

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.

22-check eval breakdown (click to expand)

Check	Pass
withAudit wrapper	✓
authenticateRequest	✓
supabaseAdmin (not client)	✓
cross-tenant guard (workspace_members + BILLING_ROLES)	✓
UUID_RX validation	✓
decryptPhi before PHI access	✓
HIPAA audit (hipaa_access_log)	✓
HIPAA non-blocking (.then)	✓
409 already-reconciled guard	✓
422 no CLP segments	✓
parse CLP segment	✓
parse SVC segment	✓
parse CAS CO (contractual) adjustment	✓
parse CAS PR (patient responsibility)	✓
GL cash_received entry	✓
GL contractual_adjustment entry	✓
GL patient_ar entry	✓
claim status map (1=paid)	✓
claim status map (4=denied)	✓
no postgres detail in 500	✓
belt-and-suspenders workspace_id eq on update	✓
marks ERA file reconciled	✓

Legacy: BFCL Routing Benchmark — v36

Mean: 100.0% PERFECT (3-seed average, seeds 2027/2028/2029, 102 cases each) — 6-tool routing

GGUF Files

File	Use	Size
`qwen3-14b-s14-q4km.gguf`	Routing — production Prism Memory (17 tools, 99.7%)	~9 GB
`qwen3-14b-v42-q4km.gguf`	Coding — Synalux TypeScript (22/22, 100%)	~9 GB
`prism-coder-14b-v36-q4km.gguf`	Routing legacy (6-tool BFCL, 100%)	~9 GB

Version History

Version	Eval	Type	Notes
S14	299/300 = 99.7% (eval_300)	Router	Production — 17-tool Prism Memory routing
v42	22/22 coding (100%)	Coder	Claim status patch; Synalux TypeScript
v36	100% BFCL (6-tool routing)	Router	Legacy 6-tool routing
v34	98.0% BFCL	Router	—

Usage

# Pull production routing model (S14 — 17-tool Prism Memory)
ollama pull dcostenco/prism-coder:14b

# Or pull GGUF directly from this repo and use with Ollama:
# FROM qwen3-14b-s14-q4km.gguf
# PARAMETER temperature 0
# PARAMETER num_ctx 8192

System Prompt (S14)

You are Synalux, a memory-augmented coding and clinical reasoning assistant. You have access to 
Prism Memory tools (session_save_ledger, session_load_context, session_search_memory, 
session_save_handoff, session_forget_memory, session_health_check, session_compact_ledger, 
session_export_memory, session_task_route, session_save_experience, session_synthesize_edges, 
session_backfill_links, knowledge_search, knowledge_forget, knowledge_upvote, knowledge_downvote, 
knowledge_set_retention) and 13 multimodal tool modules (image_gen, office, web_scraper, browser, 
tts, ocr, git, terminal, deps_scanner, hipaa, data_graph, templates, pdf_parser). Think 
step-by-step before answering. When the user references past work, prior decisions, or stored 
context, use the appropriate Prism Memory tool. Format tool calls inside <tool_call>...</tool_call> 
JSON blocks with fields 'name' and 'arguments'. If no tool is needed, answer directly in plain 
text. ABSTAIN for general programming questions, CS concepts, greetings, and capability questions.

Cascade

Tier	Model	Role
1.7B	`dcostenco/prism-coder:1b7`	Fast verify / edge cases
4B	`dcostenco/prism-coder:4b`	Mid-tier verify
14B	`dcostenco/prism-coder:14b`	Production routing
32B	`dcostenco/prism-coder:32b`	Top-tier / complex reasoning