Instructions to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1 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 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shuhulx/Qwopus3.5-4B-Coder-Fable5-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("shuhulx/Qwopus3.5-4B-Coder-Fable5-v1")
model = AutoModelForMultimodalLM.from_pretrained("shuhulx/Qwopus3.5-4B-Coder-Fable5-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuhulx/Qwopus3.5-4B-Coder-Fable5-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="shuhulx/Qwopus3.5-4B-Coder-Fable5-v1",
    max_seq_length=2048,
)

Docker Model Runner
How to use shuhulx/Qwopus3.5-4B-Coder-Fable5-v1 with Docker Model Runner:
```
docker model run hf.co/shuhulx/Qwopus3.5-4B-Coder-Fable5-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

💻 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:

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

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.