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="qwen3-30b-a3b-v1-iq4nl.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-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
Ollama
How to use dcostenco/prism-coder-32b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-32b
```

Unsloth Studio new

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

Pi new

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

prism-coder:32b — Tool Routing Model (Desktop Quality Tier)

Fine-tuned Qwen3-30B-A3B (MoE) for 6-tool routing in the Prism AAC system. Quality escalation tier in the desktop cascade: 14B → 32B → cloud Claude.

v5 (May 2026): Switched base from dense Qwen3-32B to Qwen3-30B-A3B (MoE). Same accuracy, 9 GB smaller, ~4× faster inference (only ~3B params active per token).

BFCL Routing Benchmark — v7 (Current)

Mean: 100.0% PERFECT (3-seed average, seeds 2027/2028/2029, 102 cases each)

Category	Count	Description	Accuracy
aac	12	AAC phrase requests → plain text	100%
cmpct	6	Ledger compaction	100%
edge	6	Multi-step / compound requests	100%
hand	8	Agent handoff / relay	100%
info	5	General facts → plain text	100%
irrel	10	Irrelevant / live queries → plain text	100%
know	7	Knowledge base search	100%
load	9	Session context loading	100%
pred	8	Factual / knowledge queries → plain text	100%
save	13	Session ledger save	100%
smem	12	Session memory search	100%
tran	6	Translation requests → plain text	100%

All 12 categories at 100%. No remaining failures.

Eval: MLX inference + thinking, temperature=0, 3-seed mean. Gate: ≥90% = deploy.

Full Cascade Benchmark (May 2026)

Individual BFCL scores (MLX, 3 seeds):

Model	BFCL	Size	Tier
prism-coder:8b v36	100.0% PERFECT	4.7 GB	Desktop / Mobile tier
prism-coder:14b v36	100.0% PERFECT	8.4 GB	Desktop primary tier
prism-coder:32b v7	100.0% PERFECT	16 GB	Desktop quality tier

Cascade eval: 14b → 32b → Claude Opus (102 cases × 3 seeds)

Metric	Result
Cascade accuracy	100.0% (mean, 3 seeds)
Opus-solo etalon	98.3%
Δ vs Opus	+1.7%
Traffic served by 14b	99% (101/102 cases avg)
Traffic escalated to 32b	1% (1/102 avg) — catches `save live state` → handoff edge case
Traffic reaching Opus API	0%

Fine-tuned cascade outperforms Claude Opus on edge (+16.7%) and know (+14.3%).

Version History

Version	Base	BFCL	Notes
v7 (current)	Qwen3-30B-A3B MoE	100.0% PERFECT	Fixed: "what do I know + search memory" compound → knowledge_search
v6	Qwen3-30B-A3B MoE	99.0%	Fixed MoE merge (BF16 safetensors + correct MLX→HF key mapping)
v5	Qwen3-30B-A3B MoE	97.1%	18× density fix; 9GB smaller, 4× faster vs dense
v4	Qwen3-30B-A3B MoE	92.2%	rank=32 experiment — regressed vs v3
v3	Qwen3-30B-A3B MoE	92.5%	20× reps + LR=1e-5 — hit rank bottleneck
v2	Qwen3-30B-A3B MoE	92.5%	v34 corpus + 1400 iters
v33 (dense)	Qwen3-32B dense	99.0%	Prior generation — larger/slower

Tools

The model routes between exactly 6 tools:

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

Files

File	Size	Use
`qwen3-30b-a3b-v7-iq4nl.gguf`	16 GB	Current — recommended
`qwen3-30b-a3b-v6-iq4nl.gguf`	17 GB	Previous (99.0%)
`qwen3-30b-a3b-v5-iq4nl.gguf`	17 GB	Previous (97.1%)
`qwen3-32b-v33-q6k.gguf`	25 GB	Dense predecessor (99.0%, legacy)

Usage (Ollama)

ollama run dcostenco/prism-coder:32b

Training

Base: Qwen/Qwen3-30B-A3B (HF BF16, ~57 GB)
Adapters: v6 LoRA (rank=8, scale=10, 8 layers, LR=1e-5)
Merge: Direct safetensors merge on HF BF16 base; delta = (scale/rank) × B^T A^T for attn/gate; delta[i] = (scale/rank) × B[i] A[i] for MoE experts (128 experts stacked)
Key fix: v5 merge used wrong base (MLX 4-bit, can't apply float LoRA delta) and uppercase regex lora_[AB] vs actual lowercase lora_a/lora_b adapter keys
Hardware: Apple Silicon (M-series, 64 GB RAM)

Downloads last month: 3,426

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for dcostenco/prism-coder-32b

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Qwen/Qwen3-30B-A3B-Base

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Qwen/Qwen3-30B-A3B

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