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

dcostenco commited on 21 days ago

Commit

1dde642

1 Parent(s): 4ed0ef7

docs: reframe with category-leadership story + Prism v14.0.0 algorithm citation

Browse files

Same honest framing as the 7B card: strong on AAC (95.8%), Irrelevance Detection
(91.86%), Non-Live AST (55.83%), HIPAA-safe on-device. NOT competing on BFCL V4
overall — frontier 70B+ models win that, and 14B class can't match. Pick by category.

Adds explicit link to Prism v14.0.0 algorithm-stability contract.

Files changed (1) hide show

README.md +16 -1

README.md CHANGED Viewed

@@ -28,10 +28,25 @@ tags:
   - conversational
 ---
-# Prism-Coder 14B — Function Calling + AAC Sibling (32K context)
 A fine-tune of **Qwen2.5-Coder-14B-Instruct** released **2026-05-04** as a sibling to [`prism-coder-7b`](https://huggingface.co/dcostenco/prism-coder-7b). Auto-routed for paid-tier medium-length AAC queries via the Synalux portal — keeps inference local on cloud GPU pool, $0 marginal cost vs Claude/Gemini.
 ## Sibling positioning
 | Model | Use case | Context | RAM (Q4) |

   - conversational
 ---
+# Prism-Coder 14B — On-Device AAC + Tool-Calling Sibling (32K context)
+**Category leadership story, honestly:**
+| You should pick this 14B if you need… | Score |
+|---|---|
+| **AAC realignment** for an Augmentative & Alternative Communication app | **46/48 (95.8%)** |
+| **Non-Live AST tool-call** (parsed function signatures, multi-arg) | **55.83%** |
+| **Live tool-call accuracy** in real-world agent prompts | **45.23%** |
+| **Multi-tool-call** parsing in a single user turn | **44.35%** Live Multiple AST |
+| **Irrelevance detection** — knowing when NOT to call a tool | **91.86%** ← strong |
+| HIPAA-safe **on-device** inference at 14B-class capacity (Mac / RTX 30+) | runs on 16-24 GB RAM (Q4) |
+**Not a leadership story on BFCL V4 overall** — frontier 70B+ models (GPT-4o, Claude Opus, Llama 3.3 70B) score 60-85% on overall and small open models can't match that with multi-turn / web-search / memory categories pulling the mean down. We score 19.29% overall, in line with the rest of the 14B class. **Pick by category fit.**
 A fine-tune of **Qwen2.5-Coder-14B-Instruct** released **2026-05-04** as a sibling to [`prism-coder-7b`](https://huggingface.co/dcostenco/prism-coder-7b). Auto-routed for paid-tier medium-length AAC queries via the Synalux portal — keeps inference local on cloud GPU pool, $0 marginal cost vs Claude/Gemini.
+**Powered by [Prism v14.0.0 algorithms](https://github.com/dcostenco/prism-coder/blob/main/docs/WOW_FEATURES.md):** when deployed inside PrismAAC or the Synalux portal, this model sits behind ACT-R spreading-activation phrase ranking, lesson-rate gotcha decay, and the audit-hooks postflight harvester for caregiver corrections. Model + algorithm stack together is the product.
 ## Sibling positioning
 | Model | Use case | Context | RAM (Q4) |