💻 Qwopus3.5-4B-Coder-Fable5-v1

Fable-5 trace continuation of Qwopus3.5-4B-Coder

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:

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.

Quick Start

import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM

model_id = "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1"

processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
)

model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Inspect this repo and write a Bash/Read/Edit style plan for debugging failing tests."
            }
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=768,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
)

print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Available Releases

Credits

Built on:

• Jackrong/Qwopus3.5-4B-Coder by Jackrong
• Glint-Research/Fable-5-traces by Glint-Research
• Qwen / Qwen3.5 model family
• Unsloth
• Hugging Face
• llama.cpp
• mlx-lm