Instructions to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jackrong/Qwopus3.5-27B-v3.5-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("Jackrong/Qwopus3.5-27B-v3.5-GGUF", dtype="auto")

llama-cpp-python

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jackrong/Qwopus3.5-27B-v3.5-GGUF",
	filename="Qwopus3.5-27B-v3.5-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwopus3.5-27B-v3.5-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 Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jackrong/Qwopus3.5-27B-v3.5-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 Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwopus3.5-27B-v3.5-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": "Jackrong/Qwopus3.5-27B-v3.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

SGLang

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF 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 "Jackrong/Qwopus3.5-27B-v3.5-GGUF" \
    --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": "Jackrong/Qwopus3.5-27B-v3.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Jackrong/Qwopus3.5-27B-v3.5-GGUF" \
        --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": "Jackrong/Qwopus3.5-27B-v3.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Ollama:
```
ollama run hf.co/Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
```

Unsloth Studio

How to use Jackrong/Qwopus3.5-27B-v3.5-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 Jackrong/Qwopus3.5-27B-v3.5-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 Jackrong/Qwopus3.5-27B-v3.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwopus3.5-27B-v3.5-GGUF to start chatting

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwopus3.5-27B-v3.5-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": "Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwopus3.5-27B-v3.5-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 Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M
```

Lemonade

How to use Jackrong/Qwopus3.5-27B-v3.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jackrong/Qwopus3.5-27B-v3.5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwopus3.5-27B-v3.5-GGUF-Q4_K_M

List all available models

lemonade list

🌟 Qwopus3.5-27B-v3.5

💡 Model Overview & v3.5 Design

Qwopus3.5-27B-v3.5 is a data-scaled continuation of the Qwopus3.5-27B-v3 model.

The training data in v3.5 is expanded to cover a broader range of domains, including mathematics, programming,puzzle-solving,multilingual dialogue,instruction-following, muti-turn interactions,and STEM-related tasks.

Qwopus3.5-27B-v3.5 is a reasoning-enhanced model based on Qwen3.5-27B, designed for:

🧩 Structured reasoning
🔧 Tool-augmented workflows
🔁 Multi-step agentic tasks
⚡ Token-efficient inference

Compared with Qwopus3.5-v3, 3.5 version does not introduce a new architecture, RL stage, or template redesign.

This version is trained with approximately 2× more SFT data.

🎯 Motivation & Generalization Insight

The motivation behind v3.5 comes from a simple observation:

This work is motivated by the hypothesis that scaling high-quality SFT data may further enhance the generalization ability of large language models.

In v3, Qwopus demonstrates that structured reasoning improves both accuracy and efficiency:

Structured reasoning is more effective than simply mimicking long CoT
Act-then-refine is better suited for coding and multi-step tasks
Improved reasoning structure enables more reliable use of existing knowledge

This suggests that the improvement is not simply memorization or dataset overlap. Instead, reasoning SFT helps the model:

🧠 Better utilize existing knowledge

🔍 Activate latent knowledge through structured reasoning

🏗️ Learn reasoning procedures, not just output format

🔬 Supporting Evidence

Recent work:

Ren et al., 2026 — Rethinking Generalization in Reasoning SFT (arXiv:2604.06628)

Short-epoch reasoning SFT can underestimate generalization — in-domain gains may appear early, while out-of-domain improvements often require sufficient optimization.

shows that generalization in reasoning SFT is not fixed, but conditional — depending on optimization, data quality, and model capability.

Key takeaways:

Reasoning SFT can generalize when sufficiently trained (often showing a dip → recovery pattern)
High-quality long-CoT data enables cross-domain transfer
Stronger models learn reasoning structure, not just longer outputs (14B/27B/32B)
Gains are asymmetric — reasoning improves, while safety may degrade

This suggests that reasoning SFT should be viewed as a dynamic optimization process, rather than a static training outcome.

📊 Evaluation results

Reasoning-focused SFT improves multi-step reasoning tasks, while introducing mild trade-offs on alignment-sensitive benchmarks.

A third-party benchmark report shows that Qwopus3.5-v3 achieves strong performance across reasoning-heavy tasks, especially on:

MATH500
MMLU-Pro
HumanEval
GSM8K
AIME-style reasoning tasks

However, the same results also suggest a capability trade-off: reasoning-focused SFT can improve multi-step reasoning while causing mild regressions on some alignment-sensitive or tool-oriented benchmarks.

This supports the view that Qwopus-v3 shifts the model toward stronger reasoning efficiency and problem-solving ability, rather than uniform gains across every benchmark.

🌍 Preliminary v3.5 comparison on MMLU-Pro subsets

Due to limited compute, v3.5 was evaluated on the same 280 questions used for v3, sampled from 7 selected MMLU-Pro categories.

On this subset:

Model	Correct	Total	Accuracy
v3	250	280	89.29%
v3.5	253	280	✅ 90.36%

✅ Gain: +1.07 percentage points

This suggests that scaling SFT data in v3.5 brings a small but measurable improvement on the controlled MMLU-Pro subset.

Since this is not a full MMLU-Pro evaluation, the result should be viewed as a preliminary reference, not a definitive benchmark score.

🪐 SWE / Agentic Coding Test Report

Qwopus3.5-27B-v3.5 was tested on a 44-case SWE-style capability suite covering reasoning, tool calling, structured output, context handling, multilingual responses, programming, and multi-step agentic workflows.

The Q5_K_M GGUF build achieved 43 / 44 passed tests (97.7%), including 14 / 15 programming tasks. The only failure was a unit-test-writing case involving incorrect pytest assertions. Compared with Qwopus3.5-27B-v3, which scored 42 / 44 (95.5%) on the same suite, v3.5 improved by +2.2 points.

The most important gain is in multi-step agentic coding: v3.5 successfully read source code through a tool call, diagnosed a timezone parsing bug, and proposed a fix, while v3 failed to identify the root cause. This suggests that v3.5 is a small but meaningful upgrade over v3, especially for SWE-style workflows involving tool use, code inspection, bug diagnosis, and action planning.

Throughput differences are excluded from the model-level comparison because both runs use Q5_K_M GGUF builds, where quantization choices and runtime environments can affect speed. 🏷️ Acknowledgement: Special thanks to Kyle Hessling for running and sharing the SWE-style capability tests for Qwopus3.5-27B-v3.5.
X / Twitter: @KyleHessling1

📚 Resources & Guides

👉 GitHub Repository: Jackrong-llm-finetuning-guide Visit the repo to dive into the codebase and reproduce the results locally or on Colab.

📥 Core Technical Document

🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)

The Full Pipeline: A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face.
Beginner Friendly: Includes an introductory guide to getting started with Google Colab and Unsloth.

A Note: My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. All training and testing for this project were self-funded. If you find this model or guide helpful, a Star ⭐️ on GitHub would be the greatest encouragement. Thank you! 🙏

The Claude series model optimizations are named under the Qwopus3.5 series, with the latest version being 🌟Qwopus3.5-v3.5.

⚠️ Limitations

Possible overfitting if scaling exceeds optimal regime
Reasoning may still exhibit instability in edge cases
Tool-calling performance depends on environment integration
Not all capabilities are fully benchmarked yet

🙏 Acknowledgements

Special thanks to:

Unsloth for efficient fine-tuning
Open-source datasets and community contributors
Researchers exploring reasoning SFT and generalization

📖 Citation

@misc{jackrong_qwopus35_v35,
  title        = {Qwopus3.5-27B-v3.5},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face}
}