💻 Qwopus3.5-4B-Coder-Fable5-v1
Fable-5 trace continuation of Qwopus3.5-4B-Coder
Agentic coding ·
tool use ·
debugging ·
local inference
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. |
About the Fable-5 Traces
Glint-Research/Fable-5-traces contains Claude Fable 5 coding traces.
The dataset includes fields such as:
1uid
2source_file
3session
4model
5context
6cot
7output_type
8output
9completion
10origin
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:
1Bash
2Read
3Write
4Edit
5Search
6Grep
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
1import torch
2from transformers import AutoProcessor, AutoModelForMultimodalLM
3
4model_id = "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1"
5
6processor = AutoProcessor.from_pretrained(
7 model_id,
8 trust_remote_code=True,
9)
10
11model = AutoModelForMultimodalLM.from_pretrained(
12 model_id,
13 torch_dtype=torch.bfloat16,
14 device_map="auto",
15 trust_remote_code=True,
16)
17
18messages = [
19 {
20 "role": "user",
21 "content": [
22 {
23 "type": "text",
24 "text": "Inspect this repo and write a Bash/Read/Edit style plan for debugging failing tests."
25 }
26 ],
27 }
28]
29
30inputs = processor.apply_chat_template(
31 messages,
32 add_generation_prompt=True,
33 tokenize=True,
34 return_dict=True,
35 return_tensors="pt",
36).to(model.device)
37
38outputs = model.generate(
39 **inputs,
40 max_new_tokens=768,
41 do_sample=True,
42 temperature=0.7,
43 top_p=0.95,
44)
45
46print(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