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 MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir prism-coder-1.7b dcostenco/prism-coder-1.7b

llama-cpp-python

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="gguf/prism-coder-1.7b-f16.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:F16
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:F16

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:F16
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:F16

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:F16
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-1.7b:F16

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:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-1.7b:F16

Use Docker

docker model run hf.co/dcostenco/prism-coder-1.7b:F16

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-1.7b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-1.7b:F16
```

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 MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dcostenco/prism-coder-1.7b"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dcostenco/prism-coder-1.7b"
        }
      ]
    }
  }
}

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 MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dcostenco/prism-coder-1.7b"

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

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:F16
```

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:F16

Run and chat with the model

lemonade run user.prism-coder-1.7b-F16

List all available models

lemonade list

prism-coder:1b7 — AAC Tool Router (1.7B)

Fine-tuned from Qwen3-1.7B for deterministic tool routing in the 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):

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 for the canonical prompt used in training and inference.

Downloads last month: 896

MLX

Hardware compatibility

Quantized

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

16-bit

View +2 variants

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Model tree for dcostenco/prism-coder-1.7b

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