Instructions to use dcostenco/prism-coder-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-27b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-27b",
	filename="prism-coder-27b-v3-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dcostenco/prism-coder-27b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dcostenco/prism-coder-27b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dcostenco/prism-coder-27b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dcostenco/prism-coder-27b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dcostenco/prism-coder-27b:Q4_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-27b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-27b:Q4_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-27b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-27b:Q4_K_M

Use Docker

docker model run hf.co/dcostenco/prism-coder-27b:Q4_K_M

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-27b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-27b:Q4_K_M
```

Unsloth Studio

How to use dcostenco/prism-coder-27b 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-27b 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-27b 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-27b to start chatting

How to use dcostenco/prism-coder-27b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dcostenco/prism-coder-27b:Q4_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-27b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-27b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dcostenco/prism-coder-27b:Q4_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-27b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use dcostenco/prism-coder-27b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dcostenco/prism-coder-27b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dcostenco/prism-coder-27b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use dcostenco/prism-coder-27b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-27b:Q4_K_M
```

Lemonade

How to use dcostenco/prism-coder-27b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-27b:Q4_K_M

Run and chat with the model

lemonade run user.prism-coder-27b-Q4_K_M

List all available models

lemonade list

prism-coder-27b / README.md

dcostenco

Add model card for prism-coder-27b (100% BFCL)

6cc070f verified 18 days ago

preview code

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-27B
tags:
  - prism-coder
  - qwen3.5
  - function-calling
  - mcp
  - tool-routing
  - gguf
  - qlora
  - DeltaNet
language:
  - en
  - zh

Prism Coder 27B — Qwen3.5-27B Function-Calling Model

Fine-tuned from Qwen3.5-27B for MCP tool-routing. Part of the Prism Coder fleet.

Performance

Metric	Value
BFCL Accuracy	100% × 3 seeds (345/345 test cases)
Raw accuracy	100% (no L3 correction needed)
Tokens/sec (Q4_K_M, M5 48GB)	28.5
GGUF Q4_K_M size	16 GB
Architecture	Hybrid DeltaNet (48/64 layers) + GQA (16/64)
Long context	O(n) via recurrent DeltaNet state

Training

Parameter	Value
Base model	Qwen/Qwen3.5-27B
Method	QLoRA (4-bit NF4)
LoRA rank	128, alpha=256
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Layers	All 64 (including DeltaNet)
Training data	24,798 examples (AAC 54%, tool-use 25%, safety 8%, abstention 8%)
Hardware	NVIDIA H100 PCIe 80GB
Duration	12.5 hours
Final loss	0.25
Token accuracy	93.2%
Cost	~$29

Fleet

Tag	Size	BFCL	Role
`prism-coder:2b`	2.3 GB	99.1%	Mobile / iPhone
`prism-coder:4b`	3.4 GB	100%	Verifier
`prism-coder:9b`	5.8 GB	100%	Default router
`prism-coder:27b`	16 GB	100%	Quality tier

Usage

ollama pull dcostenco/prism-coder:27b
ollama run dcostenco/prism-coder:27b "Load context for the analytics project"

Or via the Prism MCP server:

{"mcpServers": {"prism": {"command": "npx", "args": ["-y", "prism-mcp-server"]}}}

License

Apache 2.0 (same as base model)