Instructions to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf",
	filename="unsloth.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hanshan1988/unsloth-Qwen2.5-7B-banks-review-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 hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hanshan1988/unsloth-Qwen2.5-7B-banks-review-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 hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hanshan1988/unsloth-Qwen2.5-7B-banks-review-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 hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M

Use Docker

docker model run hf.co/hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M

Ollama
How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with Ollama:
```
ollama run hf.co/hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
```

Unsloth Studio

How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-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 hanshan1988/unsloth-Qwen2.5-7B-banks-review-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 hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf to start chatting

Docker Model Runner
How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with Docker Model Runner:
```
docker model run hf.co/hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M
```

Lemonade

How to use hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf:Q4_K_M

Run and chat with the model

lemonade run user.unsloth-Qwen2.5-7B-banks-review-gguf-Q4_K_M

List all available models

lemonade list

Base Model

Unsloth implementation of Qwen2.5-7B: unsloth/Qwen2.5-7B

Finetune Method

Supervised fine tuning (SFT)

Prompt Template

prompt_tmpl = """Below is a customer comment relating to their banking experience. \
Please output the banking aspects and their related sentiments expressed by the customer. \
Banking aspects must be short nouns or noun-phrases containing no more than 2 words that appear in the comment. \
Sentiments must be either positive, negative or neutral.

Output must follow the following format with NO explanations:
(credit card, positive)
(long queue, negative)
(app experience, neutral)

### Comment:
{comment}

### Response:
"""

Downloads last month: 22

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf

Base model

Qwen/Qwen2.5-7B

Finetuned

unsloth/Qwen2.5-7B

Quantized

(13)

this model

hanshan1988
/

unsloth-Qwen2.5-7B-banks-review-gguf

Base Model

Finetune Method

Prompt Template

Model tree for hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf

Dataset used to train hanshan1988/unsloth-Qwen2.5-7B-banks-review-gguf