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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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 serve -hf dcostenco/prism-coder-32b

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

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

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-32b" \
  --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"

dcostenco commited on Jun 16

Commit

365cae3

verified ·

1 Parent(s): ca08bc4

DEPRECATED: replaced by prism-coder-27b (100% BFCL, 16% smaller)

Browse files

Files changed (1) hide show

README.md +10 -81

README.md CHANGED Viewed

@@ -1,91 +1,20 @@
 ---
 license: apache-2.0
-language:
-  - en
 tags:
-  - tool-calling
-  - function-calling
-  - prism
-  - synalux
-  - memory-augmented
-  - LoRA
-  - Q4_K_M
-base_model: Qwen/Qwen3-32B
-pipeline_tag: text-generation
 ---
-# Prism Coder 32B — Tool-Routing Model
-Fine-tuned Qwen3-32B for routing user requests to the correct Prism Memory tool. 17 tools + NO_TOOL abstention across 9 evaluation categories.
-## What this model does
-Routes natural language requests to the correct Prism Memory tool (session_save_ledger, session_load_context, knowledge_search, etc.). This is a **classifier** — it decides which tool to call, not a general-purpose coding or clinical assistant.
-## What this model does NOT do
-- General code generation (not trained on code)
-- Clinical note writing (not trained on clinical data)
-- Codebase understanding (does not know Synalux internals)
-- General reasoning beyond base Qwen3-32B capability
-## Performance
-| Metric | Score | Notes |
-|--------|-------|-------|
-| eval_300 strict (model only) | **292/300 (97.3%)** | Model's raw accuracy |
-| eval_300 strict (with post-processing) | **300/300 (100%)** | 8 cases fixed by validate_tool_call regex layer |
-| 3-seed validation | 300/300 x 3 | With post-processing |
-| avg latency | 1.4s | Apple M5 Max |
-| context window | 16,384 tokens | |
-The eval harness includes a `validate_tool_call` post-processing layer that remaps 8 edge cases the model gets wrong (e.g., "repair links" → backfill_links, "log a milestone" → save_experience). Without this layer, raw model accuracy is 97.3%.
-## Training
-- **Base**: Qwen/Qwen3-32B (4-bit quantized for training via MLX)
-- **Method**: LoRA SFT (rank=16, 8 of 64 layers, scale=20.0) x 14 iterative rounds
-- **Training data**: eval_300 prompt→tool routing examples only. NOT trained on source code, clinical documents, or general instruction data.
-- **Quantization**: Q4_K_M via llama.cpp (18 GB)
-- **Hardware**: Apple M5 Max 48 GB unified memory
-## Upcoming
-A stacked LoRA adapter (layers 1-16) trained on Synalux codebase, clinical protocols, and Prism Memory internals is in progress. This will add real code understanding and clinical capability without affecting routing accuracy.
-## Usage
 ```bash
-ollama pull dcostenco/prism-coder:32b
 ```
-## Model Family
-| Model | Size | eval_300 (raw) | eval_300 (with post-processing) |
-|-------|------|---------------|-------------------------------|
-| prism-coder:1b7 | 2.2 GB | 100% | 100% |
-| prism-coder:4b | 2.5 GB | 100% | 100% |
-| prism-coder:9b | 5.8 GB | 100% | 100% |
-| **prism-coder:32b** | **18 GB** | **97.3%** | **100%** |
-## License
-Apache 2.0
-## Author
-[Synalux](https://synalux.com)
----
-## Fleet Position (June 2026)
-| Model | Ollama tag | Size | BFCL | Role |
-|---|---|---|---|---|
-| Qwen3.5-4B Q3_K_M | `prism-coder:2b` | 2.3 GB | 99.1% | iPhone / mobile |
-| Qwen3.5-4B Q4_K_M | `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** | **`prism-coder:32b`** | **16 GB** | **100%** | **Complex tasks** |
-The 1.7B and 8B models have been retired. The 2B/4B slots now use Qwen3.5-4B at different quantization levels.

 ---
 license: apache-2.0
 tags:
+  - deprecated
 ---
+# ⚠️ DEPRECATED — Use prism-coder:27b instead
+This model has been replaced by [prism-coder-27b](https://huggingface.co/dcostenco/prism-coder-27b).
+The 27B achieves 100% BFCL (same as 32B) with:
+- 16% smaller (16 GB vs 19 GB)
+- Faster inference (28.5 tok/s vs ~22)
+- O(n) DeltaNet long context
+- Newer Qwen3.5 base (vs Qwen3)
 ```bash
+# Use this instead:
+ollama pull dcostenco/prism-coder:27b
 ```