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

dcostenco commited on 24 days ago

Commit

6cc070f

verified ·

1 Parent(s): db13206

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

Browse files

Files changed (1) hide show

README.md +73 -0

README.md ADDED Viewed

	@@ -0,0 +1,73 @@

+---
+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](https://huggingface.co/Qwen/Qwen3.5-27B) for MCP tool-routing.
+Part of the [Prism Coder](https://github.com/dcostenco/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
+```bash
+ollama pull dcostenco/prism-coder:27b
+ollama run dcostenco/prism-coder:27b "Load context for the analytics project"
+```
+Or via the Prism MCP server:
+```json
+{"mcpServers": {"prism": {"command": "npx", "args": ["-y", "prism-mcp-server"]}}}
+```
+## License
+Apache 2.0 (same as base model)