Instructions to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2-7B-Multilingual-RP-GGUF",
	filename="Qwen2-7B-Multilingual-RP.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2-7B-Multilingual-RP-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 QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2-7B-Multilingual-RP-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 QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2-7B-Multilingual-RP-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 QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Qwen2-7B-Multilingual-RP-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 QuantFactory/Qwen2-7B-Multilingual-RP-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 QuantFactory/Qwen2-7B-Multilingual-RP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Qwen2-7B-Multilingual-RP-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2-7B-Multilingual-RP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2-7B-Multilingual-RP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-7B-Multilingual-RP-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2-7B-Multilingual-RP-GGUF

This is quantized version of maywell/Qwen2-7B-Multilingual-RP created using llama.cpp

Original Model Card

Have Fun :>

Qwen2-7B-Multilingual-RP

Model Info

Context Length	Parameter	Prompt Template	isErp
32k(32768)	7B	ChatML	Partly

Prompt Template

{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if not loop.last or (loop.last and message['role'] != 'assistant') %}{{'<|im_end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}

Training Details

Trained with	GPU Hour	Tokens Seen
A100 80G SXM * 8	> 1,000H	> 2B

Examples

Korean example

More examples soon.

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Downloads last month: 115

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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