Instructions to use MalxTech/Threnody-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MalxTech/Threnody-9B-GGUF 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 MalxTech/Threnody-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MalxTech/Threnody-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MalxTech/Threnody-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MalxTech/Threnody-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf MalxTech/Threnody-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf MalxTech/Threnody-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MalxTech/Threnody-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use MalxTech/Threnody-9B-GGUF with Ollama:
```
ollama run hf.co/MalxTech/Threnody-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use MalxTech/Threnody-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MalxTech/Threnody-9B-GGUF:Q4_K_M

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": "MalxTech/Threnody-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MalxTech/Threnody-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/MalxTech/Threnody-9B-GGUF:Q4_K_M
```

Lemonade

How to use MalxTech/Threnody-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MalxTech/Threnody-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Threnody-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use MalxTech/Threnody-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MalxTech/Threnody-9B-GGUF: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 MalxTech/Threnody-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MalxTech/Threnody-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MalxTech/Threnody-9B-GGUF: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 "MalxTech/Threnody-9B-GGUF: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"

Threnody-9B V2 (GGUF)

Fine-tune of Qwen/Qwen3.5-9B for multi-turn agentic coding across many CLI-agent harnesses, plus biology/chemistry. Training data comes only from June-2026+ open-weight models (Qwen3.8-27B teacher) and the base model's own verified successes. Code: https://github.com/YoMosa2009/Threnody

Status: Did not pass every gate against the base model (see table); released as the best V2 candidate. Chosen candidate: s0.3 (adapter merged at that strength). credit-limited finish: held-out harness, hard tasks and OmniAgent not measured for the chosen merge

Results (Q8_0, same build and chat template for both, temperature 0, non-thinking)

Benchmark	Qwen3.5-9B base	Threnody-9B V2
AgentLoop (14, in-harness)	12/14	11/14
AgentLoop held-out harness (opencode, 14)	10/14	not measured
AgentLoop-Hard (held-out, 30)	20/30	not measured
OmniAgent-Bench (tasks passed)	15/16	not measured
MMLU-Pro biology+chemistry (400)	75.2%	76.5%

Files

Threnody-9B-Q4_K_M.gguf
Threnody-9B-Q8_0.gguf
mmproj-Threnody-9B-F16.gguf

Run

llama-server -m Threnody-9B-Q4_K_M.gguf -ngl 99 -c 32768 -fa on -ctk q8_0 -ctv q8_0 --jinja

Optional vision: add --mmproj mmproj-Threnody-9B-F16.gguf.

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

Inference Providers NEW

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Model tree for MalxTech/Threnody-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(667)

this model