Qwopus3.5-4B-Coder-Fable5-v1-GGUF

Instructions to use AkshayCoder48/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 AkshayCoder48/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="AkshayCoder48/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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AkshayCoder48/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 serve -hf AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AkshayCoder48/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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AkshayCoder48/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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AkshayCoder48/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 "AkshayCoder48/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": "AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Ollama
How to use AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Ollama:
```
ollama run hf.co/AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use AkshayCoder48/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 AkshayCoder48/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 AkshayCoder48/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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF to start chatting

How to use AkshayCoder48/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 serve -hf AkshayCoder48/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": "AkshayCoder48/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 AkshayCoder48/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 serve -hf AkshayCoder48/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 AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-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 "AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-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"

Docker Model Runner
How to use AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF:Q4_K_M
```

Lemonade

How to use AkshayCoder48/Qwopus3.5-4B-Coder-Fable5-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AkshayCoder48/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 / README.md

AkshayCoder48

Duplicate from shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF

ce2fb03 26 days ago

preview code

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	---
	license: apache-2.0
	base_model: shuhulx/Qwopus3.5-4B-Coder-Fable5-v1
	datasets:
	- Glint-Research/Fable-5-traces
	language:
	- en
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- gguf
	- llama-cpp
	- lm-studio
	- qwen3_5
	- fable5
	- reasoning
	- agent
	- tool-use
	- function-calling
	- coder
	- coding
	- debugging
	- local-inference
	- quantized
	- conversational
	---

	<div align="center">

	# 💻 Qwopus3.5-4B-Coder-Fable5-v1 GGUF

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

	<p><b>Fable-5 traces</b> · <b>agentic coding</b> · <b>tool use</b> · <b>debugging</b></p>

	</div>

	---

	## Overview

	Qwopus3.5-4B-Coder-Fable5-v1 is a Fable-5 trace continuation of [`Jackrong/Qwopus3.5-4B-Coder`](https://huggingface.co/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`](https://huggingface.co/datasets/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

	```bash
	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

	```bash
	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:

	```bash
	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`](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) contains Claude Fable 5 coding traces.

	The dataset includes fields such as:

	```text
	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:

	```text
	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`](https://huggingface.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1) \| Python, Transformers, custom inference. \|
	\| GGUF \| [`shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-GGUF`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1-MLX-4bit) \| Apple Silicon low-memory inference. \|

	## Credits

	Built on:

	- [`Jackrong/Qwopus3.5-4B-Coder`](https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder) by Jackrong
	- [`Glint-Research/Fable-5-traces`](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) by Glint-Research
	- Qwen / Qwen3.5 model family
	- Unsloth
	- Hugging Face
	- llama.cpp
	- mlx-lm