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: 209

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for dcostenco/prism-coder-8b

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

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