Instructions to use dcostenco/prism-coder-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-1.7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-1.7b",
	filename="prism-aac-1b7-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-1.7b 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-1.7b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dcostenco/prism-coder-1.7b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

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-1.7b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

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-1.7b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

Use Docker

docker model run hf.co/dcostenco/prism-coder-1.7b:Q8_0

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-1.7b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-1.7b:Q8_0
```

Unsloth Studio new

How to use dcostenco/prism-coder-1.7b 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-1.7b 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-1.7b 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-1.7b to start chatting

Pi new

How to use dcostenco/prism-coder-1.7b 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-1.7b:Q8_0

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-1.7b:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-1.7b 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-1.7b:Q8_0

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-1.7b:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use dcostenco/prism-coder-1.7b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-1.7b:Q8_0
```

Lemonade

How to use dcostenco/prism-coder-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-1.7b:Q8_0

Run and chat with the model

lemonade run user.prism-coder-1.7b-Q8_0

List all available models

lemonade list

dcostenco commited on 13 days ago

Commit

a586bd2

verified ·

1 Parent(s): d6a986b

Update model card with training details, cascade position, and file table

Browse files

Files changed (1) hide show

README.md +37 -81

README.md CHANGED Viewed

@@ -1,102 +1,58 @@
 ---
-base_model: Qwen/Qwen3-1.7B
-pipeline_tag: text-generation
-license: apache-2.0
 language: en
 tags:
-- function-calling
-- tool-use
-- mcp
-- aac
-- on-device
-- iOS
-- prism-coder
 ---
-# prism-coder:1.7b (v36) — 100% on-device routing
-On-device MCP tool router. Runs in ~1.4 GB RAM at Q4_K_M. Built for iPhone / low-memory devices where the 14B can't load.
-## Routing accuracy — 100-case Prism eval (May 16 2026, 3-seed mean)
-| Category | Score |
-|---|---|
-| **Overall** | **100%** |
-| AAC plain-text | 100% |
-| session_compact_ledger | 100% |
-| session_load_context | 100% |
-| session_save_handoff | 100% |
-| session_save_ledger | 100% |
-| session_search_memory | 100% |
-| knowledge_search | 100% |
-| edge (compound/ambiguous) | 100% |
-| irrel (no-tool) | 100% |
-| avg latency | 0.34s |
-| invented tools | 0 |
-**100% across all 3 eval seeds (2027 / 2028 / 2029) and all 12 categories.**
-Fine-tuned via MLX LoRA (8 layers, 0.145% trainable params) on 414 targeted routing examples. Training: v36 corpus, LR 5e-6, 900 iters, val loss 0.056.
-## iOS deployment
-GGUF: `prism-aac-1b7-q4km.gguf` (1.1 GB, ~1.4 GB RAM). Integrated via llama.cpp Swift SPM into [prism-aac](https://github.com/dcostenco/prism-aac).
-## Usage
-```bash
-ollama pull dcostenco/prism-coder:1b7
-```
-## Hardware
-- **iPhone**: A14+ (iPhone 12+), ~1.4 GB RAM
-- **Mac**: any M-series
----
-### All Prism Coder models
-| Model | Accuracy | Size | Device | HuggingFace |
-|---|---|---|---|---|
-| **prism-coder:14b** | **98%** | 8.4 GB | Mac / iPad Pro 16GB | [dcostenco/prism-coder-14b](https://huggingface.co/dcostenco/prism-coder-14b) |
-| **prism-coder:8b** | **97%** | 4.7 GB | iPhone / iPad 8GB | [dcostenco/prism-coder-8b](https://huggingface.co/dcostenco/prism-coder-8b) |
-| **prism-coder:32b** | **97.3%** | 19 GB | Mac M2 Ultra+ | [dcostenco/prism-coder-32b](https://huggingface.co/dcostenco/prism-coder-32b) |
-| **prism-coder:1.7b** | **100%** | 1.1 GB | Any device / iPhone | [dcostenco/prism-coder-1.7b](https://huggingface.co/dcostenco/prism-coder-1.7b) |
-GitHub: [dcostenco/prism-coder](https://github.com/dcostenco/prism-coder) · AAC app: [dcostenco/prism-aac](https://github.com/dcostenco/prism-aac) · Portal: [synalux.ai](https://synalux.ai)
-## Get the full stack
-The model routes tool calls — but needs a backend to route TO:
 ```bash
-# Install the memory server (free, local, no API keys)
-npm install -g prism-mcp-server
-# Pull the model
-ollama pull dcostenco/prism-coder:1b7
-# Done — your AI agent now has persistent memory + 100% tool routing
 ```
-**Free tier:** local SQLite, no cloud, no account needed.
-**Synalux portal:** cloud sync, HIPAA dashboard, team access, Claude fallback → [synalux.ai](https://synalux.ai)
----
-## Prism Routing Benchmark
-This model is evaluated on the [Prism Routing Benchmark](https://github.com/dcostenco/prism-coder/tree/main/tests/benchmarks/prism-routing-100) — a 100-case, 12-category eval for MCP tool routing. Run it yourself:
-```bash
-git clone https://github.com/dcostenco/prism-coder
-cd prism-coder
-python3 tests/benchmarks/prism-routing-100/benchmark.py --models 1b7 --seed 2027
-```
-Not a general function-calling benchmark (BFCL). This measures routing precision on 6 specific MCP tools — the task these models were built for. The value is **offline reliability at zero cost**, not competing with frontier models on arbitrary APIs.
-## License
-Apache-2.0.

 ---
 language: en
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
 tags:
+  - tool-calling
+  - routing
+  - aac
+  - gguf
+  - mlx
 ---
+# prism-coder:1b7 — AAC Tool Router (1.7B)
+Fine-tuned from **Qwen3-1.7B** for deterministic tool routing in the [Prism AAC](https://github.com/dcostenco/prism-aac) system.
+**BFCL accuracy: 100%** on 100-case × 3 seeds routing benchmark (v36 corpus).
+## What it does
+Routes user messages to one of 6 tools or plain text with zero hallucination:
+| Tool | Trigger |
+|------|---------|
+| `session_load_context` | Load/fetch context for project X |
+| `session_save_ledger` | Note / jot down / log / remember |
+| `session_save_handoff` | Handoff to next agent / pass on |
+| `session_compact_ledger` | Compact/archive/trim the ledger |
+| `session_search_memory` | What did we discuss / recall session |
+| `knowledge_search` | What do I know / stored notes |
+| *(plain text)* | AAC phrases, math, facts, translation, time |
+## Deployment
+**iOS / edge** — runs on-device via llama.cpp (1.0 GB, Q4_K_M):
 ```bash
+ollama run dcostenco/prism-coder:1b7
 ```
+## Files
+| File | Size | Format |
+|------|------|--------|
+| `prism-coder-1b7-v36-q4km.gguf` | 1.0 GB | Q4_K_M GGUF (recommended) |
+| `prism-aac-1b7-q4km.gguf` | 1.0 GB | Q4_K_M GGUF (legacy name) |
+## Training
+- **Base**: Qwen3-1.7B
+- **Method**: MLX LoRA fine-tuning (mlx_lm.lora)
+- **Dataset**: v36_1b7 routing corpus (414 examples, 6-tool system prompt)
+- **Hardware**: Apple Silicon (M-series), ~4GB RAM
+- **Eval**: BFCL 100-case benchmark × 3 seeds → **100%**
+## System prompt
+Uses the 13-rule routing system prompt. See [Prism AAC](https://github.com/dcostenco/prism-aac) for the canonical prompt used in training and inference.