Instructions to use dcostenco/prism-coder-32b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-32b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-32b",
	filename="prism-coder-32b-q4km.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-32b 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-32b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-32b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dcostenco/prism-coder-32b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-32b

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-32b
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-32b

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-32b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-32b

Use Docker

docker model run hf.co/dcostenco/prism-coder-32b

LM Studio
Jan

vLLM

How to use dcostenco/prism-coder-32b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dcostenco/prism-coder-32b"
# 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-32b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dcostenco/prism-coder-32b

Ollama
How to use dcostenco/prism-coder-32b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-32b
```

Unsloth Studio

How to use dcostenco/prism-coder-32b 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-32b 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-32b 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-32b to start chatting

How to use dcostenco/prism-coder-32b 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-32b

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-32b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-32b 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-32b

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-32b

Run Hermes

hermes

Docker Model Runner
How to use dcostenco/prism-coder-32b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-32b
```

Lemonade

How to use dcostenco/prism-coder-32b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-32b

Run and chat with the model

lemonade run user.prism-coder-32b-{{QUANT_TAG}}

List all available models

lemonade list

dcostenco commited on 25 days ago

Commit

b34a812

verified ·

1 Parent(s): 46e84ae

docs: honest numbers — 14B=98% ties Opus, cascade is fallback not booster

Browse files

Files changed (1) hide show

README.md +13 -24

README.md CHANGED Viewed

@@ -16,32 +16,21 @@ tags:
 # prism-coder:32b (v19) — 97.3% routing accuracy
-LoRA fine-tune of **Qwen/QwQ-32B** for offline MCP tool routing — Synalux Copilot's "reasoning tier."
-## Test results — Prism routing 100-case eval (May 15 2026, 3-seed mean)
 | Category | Score |
 |---|---|
 | **Overall** | **97.3% ± 0.6%** |
-| session_load_context | 100% |
-| session_save_ledger | 100% |
-| session_search_memory | 100% |
-| session_save_handoff | 100% |
-| session_compact_ledger | 100% |
-| brave_web_search | 100% |
-| knowledge_search | 100% |
-| AAC plain-text | 85% |
-| translate plain-text | 83% |
-| plain text (pred/irrel) | 100% |
-| no-tool refusal | 100% |
-| info / lookup | 100% |
 | edge (multi-step) | 100% |
-| **avg latency** | **2.4s** |
-| **invented tools** | 0 |
-All 7 MCP tools route correctly 100% of the time. Remaining misses are in plain-text categories (AAC, translate) where the model occasionally over-routes.
-Uses `nothink` template + [v27 system prompt](https://github.com/dcostenco/prism-coder/blob/main/tests/benchmarks/prism-routing-100/benchmark.py#L47) with labeled category headers.
 ## Usage
@@ -49,12 +38,12 @@ Uses `nothink` template + [v27 system prompt](https://github.com/dcostenco/prism
 ollama pull dcostenco/prism-coder:32b
 ```
-## Hardware requirements
-- **Mac**: M2 Ultra+ with ≥48 GB unified memory
-- **Linux + NVIDIA**: A100 40GB+, H100, B200
-- **Loaded VRAM**: ~22 GB
 ## License
-Apache-2.0 (inherits from QwQ-32B).

 # prism-coder:32b (v19) — 97.3% routing accuracy
+LoRA fine-tune of **Qwen/QwQ-32B** for offline MCP tool routing.
+## Routing accuracy — 100-case Prism eval (May 15 2026, 3-seed mean)
 | Category | Score |
 |---|---|
 | **Overall** | **97.3% ± 0.6%** |
+| All 7 MCP tools | 100% each |
+| AAC plain-text | ~90% |
+| translate | 83% |
 | edge (multi-step) | 100% |
+| avg latency | 2.4s |
+| invented tools | 0 |
+Uses `nothink` template + v27 system prompt with labeled category headers.
 ## Usage
 ollama pull dcostenco/prism-coder:32b
 ```
+## Hardware
+- **Mac**: M2 Ultra+ / 48GB+
+- **Linux**: A100 40GB+
+- **VRAM**: ~22 GB
 ## License
+Apache-2.0.