Instructions to use Alumin-Hydro/Qwen3.5-9B-Physics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Alumin-Hydro/Qwen3.5-9B-Physics with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Alumin-Hydro/Qwen3.5-9B-Physics", filename="qwen3.5-9b-physics-q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Alumin-Hydro/Qwen3.5-9B-Physics with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics: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 Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics: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 Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
Use Docker
docker model run hf.co/Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Alumin-Hydro/Qwen3.5-9B-Physics with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Alumin-Hydro/Qwen3.5-9B-Physics" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Alumin-Hydro/Qwen3.5-9B-Physics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
- Ollama
How to use Alumin-Hydro/Qwen3.5-9B-Physics with Ollama:
ollama run hf.co/Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
- Unsloth Studio new
How to use Alumin-Hydro/Qwen3.5-9B-Physics 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 Alumin-Hydro/Qwen3.5-9B-Physics 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 Alumin-Hydro/Qwen3.5-9B-Physics to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Alumin-Hydro/Qwen3.5-9B-Physics to start chatting
- Docker Model Runner
How to use Alumin-Hydro/Qwen3.5-9B-Physics with Docker Model Runner:
docker model run hf.co/Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
- Lemonade
How to use Alumin-Hydro/Qwen3.5-9B-Physics with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-9B-Physics-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_MUse 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 Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_MBuild 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 Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_MUse Docker
docker model run hf.co/Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_MQwen3.5-9B-Physics
A parameter-efficient fine-tuned LoRA adapter built on Qwen/Qwen3.5-9B, optimized for physics problem-solving. Trained with LLaMA Factory on the camel_physics dataset.
This repository provides both the lightweight LoRA adapter and the standalone quantized GGUF model for local deployment.
Model Details
Base Model: Qwen/Qwen3.5-9B
Fine-tuning Method: Supervised Fine-Tuning (SFT) + LoRA
Training Framework: LLaMA Factory
Training Dataset: camel_ai/physics (5k curated physics question-answer samples)
Training Precision: 4-bit quantized training (bitsandbytes, BF16)
LoRA Hyperparameters:
LoRA Rank: 16
LoRA Alpha: 32
LoRA Dropout: 0.0
Quantized Model: Q4_K_M GGUF (6.4GB)
Model Capabilities
Specialized in high school and undergraduate physics problem-solving, formula derivation and conceptual analysis
Preserves the general conversational and reasoning ability of the original Qwen3.5-9B base model
Dual deployment support: lightweight LoRA adapter for development and optimized GGUF model for local inference
Compatible with Transformers, PEFT, llama.cpp and Ollama
Usage
1. Load LoRA Adapter (For Development)
Combine the LoRA adapter with the official Qwen3.5-9B base model for full fine-tuned capabilities:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_id = "Qwen/Qwen3.5-9B"
lora_model_id = "Alumin-Hydro/Qwen3.5-9B-Physics"
# Load base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(
base_model_id,
device_map="auto",
torch_dtype="auto"
)
# Load physics LoRA adapter
model = PeftModel.from_pretrained(model, lora_model_id)
model.eval()
2. Run Quantized GGUF Model (For Local Deployment)
A standalone Q4_K_M quantized GGUF model is provided for fast local inference without additional dependencies.
Ollama Deployment
ollama create qwen3.5-9b-physics -f Modelfile
ollama run qwen3.5-9b-physics
llama.cpp Deployment
Directly load qwen3.5-9b-physics-q4_K_M.gguf with llama.cpp or any GGUF-compatible inference framework.
File Description
adapter_model.safetensors&adapter_config.json: Lightweight LoRA adapter (~169MB)qwen3.5-9b-physics-q4_K_M.gguf: Merged & quantized full model (Q4_K_M, 6.4GB)Modelfile: Official Ollama configuration file
License
This model follows the Apache 2.0 license, consistent with the original Qwen3.5-9B base model.
- Downloads last month
- 215
4-bit

Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M# Run inference directly in the terminal: llama-cli -hf Alumin-Hydro/Qwen3.5-9B-Physics:Q4_K_M