---
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