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:Q8_0
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

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:Q8_0
# Run inference directly in the terminal:
llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

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:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

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:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dcostenco/prism-coder-1.7b:Q8_0

Use Docker

docker model run hf.co/dcostenco/prism-coder-1.7b:Q8_0

LM Studio
Jan
Ollama
How to use dcostenco/prism-coder-1.7b with Ollama:
```
ollama run hf.co/dcostenco/prism-coder-1.7b:Q8_0
```

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:Q8_0

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:Q8_0"
        }
      ]
    }
  }
}

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:Q8_0

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:Q8_0

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:Q8_0
```

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:Q8_0

Run and chat with the model

lemonade run user.prism-coder-1.7b-Q8_0

List all available models

lemonade list

prism-coder:1b7 — 17-Tool Memory Agent (Always-Fits Tier)

Fine-tuned Qwen3-1.7B for full Prism Memory tool routing in the Prism Coder system. Primary deployment: any device via llama.cpp GGUF — the ultra-lightweight tier.

eval_300 Benchmark — swe43 (Current)

300/300 × 3 shuffled runs = 100.0%, 0 flaky

Category	Count	Description	Accuracy
natural_phrasing	50	Natural language → correct tool	100%
adversarial_trap	70	Coding/CS questions → plain text (no tool)	100%
disambiguation	40	Ambiguous session vs knowledge ops	100%
edge_case	25	Self-description, capability queries → plain text	100%
verifier	25	Verify-then-act chains	100%
param_extraction	25	Extract project/query from prompt	100%
cascade	25	Multi-step tool chains	100%
multi_intent	20	Compound instructions	100%
abstention	20	Greetings, math, creative requests → plain text	100%

300 test cases, 3 shuffled runs, temperature=0, 0 hallucinations across all runs.

Tools

Routes to 17 Prism Memory tools + knows when NOT to call any tool:

Tool	Trigger
`session_load_context`	Load/resume project context, "starting fresh"
`session_save_ledger`	Log/record completed work
`session_save_handoff`	Create handoff note for next session
`session_search_memory`	Recall prior discussions
`session_forget_memory`	Delete a memory entry
`session_health_check`	Check session system health
`session_compact_ledger`	Compact/prune session ledger
`session_export_memory`	Export session data
`session_task_route`	Route task: local vs cloud
`session_save_experience`	Save a notable experience
`session_synthesize_edges`	Build session graph edges
`session_backfill_links`	Repair dangling session links
`knowledge_search`	Search stored knowledge base
`knowledge_forget`	Remove a knowledge entry
`knowledge_upvote`	Upvote knowledge entry
`knowledge_downvote`	Downvote knowledge entry
`knowledge_set_retention`	Set retention policy

Abstains (plain text) for: coding questions, CS concepts, arithmetic, greetings, capability queries, creative requests, general knowledge.

Version History

Version	eval_300	Notes
swe43	300/300 × 3 runs = 100.0%	Fresh rank=32 LoRA + `<think>` routing, Q8_0 GGUF
swe30	280/300 = 93.3%	Q8_0 first round (fixed Q4KM quantization erasure)
v43l	203/300 = 67.7%	Baseline before SWE training
v42	100% BFCL 6-tool	Previous 6-tool routing model

Key Training Insights

Q8_0 quantization required — Q4KM erased LoRA deltas for soft abstain patterns (87%→93% at R30)
Adapter saturation — After 39 cumulative rounds at rank=8, adapter was saturated. Fresh rank=32 on R39-merged base broke plateau in one round (93.3%→99.7%)
<think> routing blocks — Added CoT reasoning to abstain examples activates Qwen3's pretrained thinking circuit, providing explicit gradient path for the routing decision

Model Details

Base: Qwen/Qwen3-1.7B → merged through 43 SWE training rounds
Format: GGUF Q8_0 (2.2 GB)
Context: 8,192 tokens
Final adapter: MLX LoRA rank=32, all 28 layers, LR=3e-6→8e-7, 1,267 train rows/round
Total training: 43 rounds of cumulative SFT + 4 fresh rank=32 rounds

Usage

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

Or via the Synalux Prism MCP server which routes tool calls automatically.

Downloads last month: 2,234

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

8-bit

View +4 variants

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