Instructions to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF",
	filename="Qwopus3.5-4B-Coder-Fable5-v1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF: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 shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF: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 shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Ollama
How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Ollama:
```
ollama run hf.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF 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 shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF 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 shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF to start chatting

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF: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": "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-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 shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
```

Lemonade

How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwopus3.5-4B-Coder-Fable5-v1-GGUF-Q4_K_M

List all available models

lemonade list

💻 Qwopus3.5-4B-Coder-Fable5-v1 GGUF

GGUF builds for llama.cpp, LM Studio, and local inference

Fable-5 traces · agentic coding · tool use · debugging

Overview

Qwopus3.5-4B-Coder-Fable5-v1 is a Fable-5 trace continuation of Jackrong/Qwopus3.5-4B-Coder.

The base model, Qwopus3.5-4B-Coder, is a compact Qwen3.5-based coding model trained for reasoning, tool use, function calling, coding workflows, and agent-style behavior.

This release continues that model on Glint-Research/Fable-5-traces, a dataset of Claude Fable 5 local coding-agent traces. The dataset is heavily oriented around tool-use trajectories, repository work, local command context, code editing, debugging loops, and <think>-style reasoning completions.

The result is a small local coding-agent model intended for:

Area	Description
Tool-use workflows	Bash, Read, Write, Edit, repo inspection, and action traces.
Debugging	Failing tests, stack traces, root-cause analysis, and patch planning.
Trace-style reasoning	Long-form planning and `<think>` style reasoning traces.
Local agents	Hermes-style, Claude-Code-style, OpenCode-style, and LM Studio workflows.

Files

Typical GGUF files:

Qwopus3.5-4B-Coder-Fable5-v1-Q4_K_M.gguf
Qwopus3.5-4B-Coder-Fable5-v1-Q5_K_M.gguf
Qwopus3.5-4B-Coder-Fable5-v1-mmproj-BF16.gguf

Which file should I use?

File	Use case
`Q4_K_M`	Best default. Small, fast, good quality.
`Q5_K_M`	Better quality while still compact.
`Q8_0`	Higher quality, larger memory use, if included.
`mmproj-BF16`	Multimodal projector for compatible runtimes.

llama.cpp

llama-cli \
  -m Qwopus3.5-4B-Coder-Fable5-v1-Q5_K_M.gguf \
  -p "Write a Bash/Read/Edit style plan for debugging a failing Python repo." \
  -n 768 \
  --temp 0.7 \
  --top-p 0.95

llama.cpp Server

llama-server \
  -m Qwopus3.5-4B-Coder-Fable5-v1-Q5_K_M.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --ctx-size 8192

Then call it with an OpenAI-compatible client:

curl -X POST "http://localhost:8080/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "Qwopus3.5-4B-Coder-Fable5-v1-Q5_K_M.gguf",
    "messages": [
      {"role": "user", "content": "Write a tool-use plan for debugging a Python repo."}
    ],
    "temperature": 0.7,
    "top_p": 0.95
  }'

About the Fable-5 Traces

Glint-Research/Fable-5-traces contains Claude Fable 5 coding traces.

The dataset includes fields such as:

uid
source_file
session
model
context
cot
output_type
output
completion
origin

The examples are not simple chat pairs. They are multi-step agent trajectories with local development context, reasoning traces, and tool-use outputs.

Common patterns in the dataset include:

user coding requests
local-command caveats
repository inspection
Bash command usage
file reads
file writes
edits
debugging passes
playtesting / validation loops
<think>...</think> reasoning traces
tool-use completions

A large portion of the dataset is tool_use style data, which makes it especially relevant for local coding agents and developer automation.

Capabilities

Agentic coding

Designed for coding-agent loops where the model must inspect a repo, plan work, call tools, edit files, and validate changes.

Tool-use style outputs

Works well with prompts that expose structured tools such as:

Bash
Read
Write
Edit
Search
Grep

Debugging and repair

Useful for:

finding likely failing files
explaining stack traces
planning test commands
proposing minimal patches
iterating after errors

Local-first deployment

The release includes Transformers, GGUF, MLX, and MLX 4-bit formats so it can run in Python, llama.cpp, LM Studio, and Apple Silicon workflows.

Available Releases

Release	Repo	Best for
Transformers / Safetensors	`shuhulx/Qwopus3.5-4B-Coder-Fable5-v1`	Python, Transformers, custom inference.
GGUF	`shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF`	llama.cpp, LM Studio, local CPU/GPU inference.
MLX	`shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-MLX`	Apple Silicon full MLX inference.
MLX 4-bit	`shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-MLX-4bit`	Apple Silicon low-memory inference.