Instructions to use dcostenco/prism-coder-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-8b",
	filename="prism-aac-8b-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-8b 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-8b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-8b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dcostenco/prism-coder-8b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-8b

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-8b
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-8b

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-8b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-8b

Use Docker

docker model run hf.co/dcostenco/prism-coder-8b

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-8b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-8b
```

Unsloth Studio new

How to use dcostenco/prism-coder-8b 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-8b 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-8b 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-8b to start chatting

Pi new

How to use dcostenco/prism-coder-8b 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-8b

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-8b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-8b 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-8b

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-8b

Run Hermes

hermes

Docker Model Runner
How to use dcostenco/prism-coder-8b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-8b
```

Lemonade

How to use dcostenco/prism-coder-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-8b

Run and chat with the model

lemonade run user.prism-coder-8b-{{QUANT_TAG}}

List all available models

lemonade list

prism-coder:8b — Tool Routing Model (iOS Tier)

Fine-tuned Qwen3-8B for 6-tool routing in the Prism AAC system. Primary deployment: iOS/edge via llama.cpp GGUF.

Versions

Version	File	BFCL	Notes
v31	`qwen3-8b-v31-q4km.gguf`	95.1%	Surgical smem/know boundary + save fixes
v30	`qwen3-8b-v30-q4km.gguf`	95.0%	Routing corpus v36_1b7

BFCL Routing Benchmark (v31)

95.1% — 3-seed mean (seeds 2027/2028/2029), 100 cases each
Eval: MLX inference, greedy (temp=0), Qwen3 thinking suppressed
Gate: ≥90% = deploy

Tools

session_load_context — load/fetch/resume project context
session_save_ledger — note/log/remember/record
session_save_handoff — handoff/relay/next-agent transition
session_compact_ledger — compact/archive ledger
session_search_memory — recall past sessions/conversations
knowledge_search — search stored notes/knowledge base

Cascade Role

iOS fallback tier. Desktop cascade uses 14B → 32B → cloud Claude. 8B handles edge/offline scenarios where RAM < 6GB.

Usage (Ollama)

ollama pull dcostenco/prism-coder:8b-v30
ollama run dcostenco/prism-coder:8b-v30

Training

Base: Qwen3-8B (MLX 4-bit)
Framework: MLX-LM LoRA (8 layers, batch 2, grad-checkpoint)
v31 data: 361 train / 41 valid (targeted smem/know boundary augmentations)
v31 LR: 3e-6 (surgical, 200 iters)
Peak memory: 7.0 GB

Downloads last month: 421

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for dcostenco/prism-coder-8b

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(280)

this model