Instructions to use dcostenco/prism-coder-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-1.7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dcostenco/prism-coder-1.7b",
	filename="prism-aac-1b7-q4km.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-1.7b 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-1.7b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dcostenco/prism-coder-1.7b
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b

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-1.7b
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-1.7b

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-1.7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-1.7b

Use Docker

docker model run hf.co/dcostenco/prism-coder-1.7b

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-1.7b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-1.7b
```

Unsloth Studio new

How to use dcostenco/prism-coder-1.7b 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-1.7b 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-1.7b 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-1.7b to start chatting

Pi new

How to use dcostenco/prism-coder-1.7b 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-1.7b

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-1.7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dcostenco/prism-coder-1.7b 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-1.7b

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-1.7b

Run Hermes

hermes

Docker Model Runner
How to use dcostenco/prism-coder-1.7b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-1.7b
```

Lemonade

How to use dcostenco/prism-coder-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dcostenco/prism-coder-1.7b

Run and chat with the model

lemonade run user.prism-coder-1.7b-{{QUANT_TAG}}

List all available models

lemonade list

prism-coder-1.7b / README.md

dcostenco

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	---
	language: en
	license: apache-2.0
	tags:
	- tool-routing
	- function-calling
	- prism-aac
	- qwen3
	- gguf
	base_model: Qwen/Qwen3-1.7B
	---

	# prism-coder:1.7b — Tool Routing Model (Always-Fits Tier)

	Fine-tuned Qwen3-1.7B for 6-tool routing in the [Prism AAC](https://github.com/dcostenco/prism-aac) system.
	Primary deployment: any iOS device via llama.cpp GGUF — the guaranteed fallback for all device tiers.

	## BFCL Routing Benchmark — v42 (Current)

	Mean: 100.0% (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% \|

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

	## Version History

	\| Version \| BFCL \| Notes \|
	\|---------\|------\|-------\|
	\| v42 \| 100.0% \| Fixed 4 deterministic failures: cmpct tool name, compound edge, write-code irrel, pull-context load \|
	\| v41 \| 96.1% \| Proper safetensors merge — fixes mlx_lm.fuse LoRA loss \|
	\| v36 \| 94.1% \| LoRA rank=16, all 28 layers, mask-prompt \|
	\| v19 \| ~88% \| Baseline 1.7B routing \|

	## Tools

	The model routes to exactly 6 tools:

	\| Tool \| Trigger \|
	\|------\|---------\|
	\| `session_load_context` \| Load/resume/pull project context \|
	\| `session_save_ledger` \| Note/log/record/remember something \|
	\| `session_save_handoff` \| Pass state to next agent/session \|
	\| `session_compact_ledger` \| Compact/shrink/prune ledger \|
	\| `session_search_memory` \| Recall prior session discussions \|
	\| `knowledge_search` \| Search stored knowledge base ("what do I know") \|

	Plain text (no tool) for: AAC phrases, translations, weather, general facts, code/regex/functions, math.

	## Model Details

	- Base: Qwen/Qwen3-1.7B
	- Format: GGUF Q4_K_M (~1.2 GB)
	- Context: 32,768 tokens
	- Training: MLX LoRA, rank=16, all 28 layers, 800 iters, LR=5e-5, v42 corpus (1028 train / 79 valid)
	- Merge: direct safetensors merge (scale/rank × B.T @ A.T) → llama.cpp convert → Q4_K_M quantization

	## Usage

	```bash
	ollama pull dcostenco/prism-coder:1b7
	ollama run prism-coder:1b7
	```

	Or in [Prism AAC](https://github.com/dcostenco/prism-aac) — the app downloads and loads this model automatically on devices with <8 GB RAM.

	---
	language: en
	license: apache-2.0
	tags:
	- tool-routing
	- function-calling
	- prism-aac
	- qwen3
	- gguf
	base_model: Qwen/Qwen3-1.7B
	---

	# prism-coder:1.7b — Tool Routing Model (Always-Fits Tier)

	Fine-tuned Qwen3-1.7B for 6-tool routing in the [Prism AAC](https://github.com/dcostenco/prism-aac) system.
	Primary deployment: any iOS device via llama.cpp GGUF — the guaranteed fallback for all device tiers.

	## BFCL Routing Benchmark — v42 (Current)

	Mean: 100.0% (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% \|

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

	## Version History

	\| Version \| BFCL \| Notes \|
	\|---------\|------\|-------\|
	\| v42 \| 100.0% \| Fixed 4 deterministic failures: cmpct tool name, compound edge, write-code irrel, pull-context load \|
	\| v41 \| 96.1% \| Proper safetensors merge — fixes mlx_lm.fuse LoRA loss \|
	\| v36 \| 94.1% \| LoRA rank=16, all 28 layers, mask-prompt \|
	\| v19 \| ~88% \| Baseline 1.7B routing \|

	## Tools

	The model routes to exactly 6 tools:

	\| Tool \| Trigger \|
	\|------\|---------\|
	\| `session_load_context` \| Load/resume/pull project context \|
	\| `session_save_ledger` \| Note/log/record/remember something \|
	\| `session_save_handoff` \| Pass state to next agent/session \|
	\| `session_compact_ledger` \| Compact/shrink/prune ledger \|
	\| `session_search_memory` \| Recall prior session discussions \|
	\| `knowledge_search` \| Search stored knowledge base ("what do I know") \|

	Plain text (no tool) for: AAC phrases, translations, weather, general facts, code/regex/functions, math.

	## Model Details

	- Base: Qwen/Qwen3-1.7B
	- Format: GGUF Q4_K_M (~1.2 GB)
	- Context: 32,768 tokens
	- Training: MLX LoRA, rank=16, all 28 layers, 800 iters, LR=5e-5, v42 corpus (1028 train / 79 valid)
	- Merge: direct safetensors merge (scale/rank × B.T @ A.T) → llama.cpp convert → Q4_K_M quantization

	## Usage

	```bash
	ollama pull dcostenco/prism-coder:1b7
	ollama run prism-coder:1b7
	```

	Or in [Prism AAC](https://github.com/dcostenco/prism-aac) — the app downloads and loads this model automatically on devices with <8 GB RAM.