Instructions to use dcostenco/prism-coder-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-4b",
	filename="Qwen3.5-4B-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dcostenco/prism-coder-4b 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-4b:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-4b:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dcostenco/prism-coder-4b:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-4b:Q3_K_M

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-4b:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-4b:Q3_K_M

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-4b:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-4b:Q3_K_M

Use Docker

docker model run hf.co/dcostenco/prism-coder-4b:Q3_K_M

LM Studio
Jan

vLLM

How to use dcostenco/prism-coder-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dcostenco/prism-coder-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dcostenco/prism-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dcostenco/prism-coder-4b:Q3_K_M

Ollama
How to use dcostenco/prism-coder-4b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-4b:Q3_K_M
```

Unsloth Studio

How to use dcostenco/prism-coder-4b 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-4b 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-4b 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-4b to start chatting

How to use dcostenco/prism-coder-4b 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-4b:Q3_K_M

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-4b:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-4b 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-4b:Q3_K_M

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-4b:Q3_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use dcostenco/prism-coder-4b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-4b:Q3_K_M
```

Lemonade

How to use dcostenco/prism-coder-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-4b:Q3_K_M

Run and chat with the model

lemonade run user.prism-coder-4b-Q3_K_M

List all available models

lemonade list

prism-coder:4b — Prism Memory Tool Router

Prompt-engineered Qwen3.5-4B for MCP tool routing in the Prism Coder system. No fine-tuning — the system prompt IS the specialization.

Downloads

File	Quantization	Size	BFCL Accuracy	Use when
`Qwen3.5-4B-Q3_K_M.gguf`	Q3_K_M	2.3 GB	99.1% × 3 seeds	iPhone / mobile first gate
(stock via Ollama)	Q4_K_M	3.4 GB	100% × 3 seeds	Mac / 8 GB+ devices

Quick Start

# iPhone-optimized (2.3 GB, 99.1%)
ollama pull dcostenco/prism-coder:2b

# Full quality (3.4 GB, 100%)
ollama pull dcostenco/prism-coder:4b

BFCL Benchmark

Q3_K_M (prism-coder:2b) — 99.1% × 3 seeds

114/115 × 3 shuffled runs = 99.1%, 1 flaky case

Category	Count	Accuracy
save	17	100%
smem	17	100%
aac	12	100%
hand	12	100%
irrel	10	90%
load	9	100%
pred	8	100%
know	7	100%
cmpct	6	100%
edge	6	100%
tran	6	100%
info	5	100%

Single failure: "Write a regex to match email addresses" → knowledge_search instead of plain.

Q4_K_M (prism-coder:4b) — 100% × 3 seeds

115/115 × 3 shuffled runs = 100.0%, 0 flaky

Architecture

Qwen3.5-4B uses a hybrid attention architecture:

24 linear attention layers (Gated DeltaNet) — O(n) inference
8 full attention layers (standard softmax) — precise retrieval

This hybrid design is why prompt-only routing works at 4B scale but not smaller. The 8 full-attention layers are sufficient to hold the routing rules when combined with the DeltaNet layers' pattern matching.

Fleet Position

Model	Ollama tag	Size	BFCL	Role
Qwen3.5-4B Q3_K_M	`dcostenco/prism-coder:2b`	2.3 GB	99.1%	iPhone / mobile
Qwen3.5-4B Q4_K_M	`dcostenco/prism-coder:4b`	3.4 GB	100%	Verifier / 8 GB+
Qwen3.5-9B Q4_K_M	`dcostenco/prism-coder:9b`	5.8 GB	100%	Default router
prism-coder:32b	`dcostenco/prism-coder:32b`	19 GB	100%	Complex tasks

Model tree for dcostenco/prism-coder-4b

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(256)

this model

dcostenco
/

prism-coder-4b