Instructions to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Oysiyl/qwen3.5-27b-unslop-good-lora-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("Oysiyl/qwen3.5-27b-unslop-good-lora-v1") model = AutoModelForMultimodalLM.from_pretrained("Oysiyl/qwen3.5-27b-unslop-good-lora-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]:])) - llama-cpp-python
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Oysiyl/qwen3.5-27b-unslop-good-lora-v1", filename="gguf/q2_k_gguf_gguf/Qwen3.5-27B.BF16-00002-of-00002.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1: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 Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1: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 Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
Use Docker
docker model run hf.co/Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Oysiyl/qwen3.5-27b-unslop-good-lora-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": "Oysiyl/qwen3.5-27b-unslop-good-lora-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
- SGLang
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-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 "Oysiyl/qwen3.5-27b-unslop-good-lora-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": "Oysiyl/qwen3.5-27b-unslop-good-lora-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 "Oysiyl/qwen3.5-27b-unslop-good-lora-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": "Oysiyl/qwen3.5-27b-unslop-good-lora-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Ollama:
ollama run hf.co/Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
- Unsloth Studio
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-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 Oysiyl/qwen3.5-27b-unslop-good-lora-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 Oysiyl/qwen3.5-27b-unslop-good-lora-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Oysiyl/qwen3.5-27b-unslop-good-lora-v1 to start chatting
- Pi
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1: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": "Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Oysiyl/qwen3.5-27b-unslop-good-lora-v1: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 Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Docker Model Runner:
docker model run hf.co/Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
- Lemonade
How to use Oysiyl/qwen3.5-27b-unslop-good-lora-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Oysiyl/qwen3.5-27b-unslop-good-lora-v1:Q4_K_M
Run and chat with the model
lemonade run user.qwen3.5-27b-unslop-good-lora-v1-Q4_K_M
List all available models
lemonade list
qwen3.5-27b-unslop-good-lora-v1
Unslop rewrite adapter focused on reducing hype/corporate phrasing while preserving meaning.
Model summary
- Repo:
Oysiyl/qwen3.5-27b-unslop-good-lora-v1 - Base model:
unsloth/Qwen3.5-27B - Adapter type: LoRA
- Pipeline: text generation / rewrite style transfer
- Current downloads (snapshot): 2150
Intended use
- Rewrite AI-sounding drafts into cleaner, more natural prose.
- Keep meaning and key facts intact.
- Use as a post-processing layer for longform and social text cleanup.
Limitations
- Can still over-rewrite some passages.
- Not guaranteed to improve factual accuracy.
- Should be human-reviewed for fidelity-sensitive outputs.
Evaluation notes
This card records this model as part of the Unslop family with a common quality goal: preserve meaning, reduce hype, and avoid hallucinated additions.
Usage (PEFT)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "unsloth/Qwen3.5-27B"
adapter = "Oysiyl/qwen3.5-27b-unslop-good-lora-v1"
tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter)
Training loss vs training progress
Loss curve included as optimization check; qualitative rewrite fidelity remains the decision metric.
Recommended evaluation sample
Use this full fiction passage for held-out testing:
The wind rose all at once, as if the mountain itself had inhaled. She stood at the edge of the pass, cloak snapping like a torn banner behind her, staring down at the valley where the last lights were going out one by one. Each flame extinguished felt deliberate—an answer to a question she had been too afraid to ask. Somewhere below, bells began to ring, not in warning, but in mourning. They already knew. “Don’t,” he said, breathless, reaching for her arm. His fingers brushed fabric, then air. “If you cross that line, there’s no coming back.” She didn’t turn. The cold burned her lungs, sharpened her thoughts. “There was never a way back,” she replied softly. “Only a way forward you were willing to pretend didn’t exist.” Thunder rolled, low and ancient. For a moment, the sky fractured into white fire, and in that instant he saw her face—resolved, terrified, incandescent with purpose. Not the girl he had sworn to protect, but the woman the world had demanded she become. “You’ll die,” he said. She smiled then, finally facing him, eyes bright with unshed tears. “So will everyone,” she answered. “The difference is what we leave burning when we’re gone.” She stepped forward. The mountain exhaled.
Full observed output on that sample
The wind surged suddenly, as though the mountain had drawn a breath. At the edge of the pass, her cloak snapped behind her like a tattered flag. Below, the last lights of the valley flickered out one by one; each extinguished flame felt intentional, an answer to a question she hadn't dared to voice. Then came the bells, tolling not for alarm, but for grief. They knew.
"Don't," he gasped, lunging for her arm. His fingers caught cloth, then empty air. "If you cross that line, you can't come back."
She didn't look at him. The freezing air seared her lungs, clearing her mind. "There was never a way back," she said quietly. "Just a path forward you chose to ignore."
A low, ancient thunder rumbled. A flash of white fire split the sky, illuminating her face for a heartbeat—determined, frightened, alight with resolve. This wasn't the girl he had promised to shield, but the woman the world had forced her to be.
"You'll die," he said.
She turned to him then, smiling through eyes shimmering with unshed tears. "We all will," she replied. "The only thing that matters is what we leave burning."
She stepped forward. The mountain breathed out.
- Downloads last month
- 58