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

Libraries

How to use maywell/Qwen2-7B-Multilingual-RP with Transformers:

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

pipe = pipeline("text-generation", model="maywell/Qwen2-7B-Multilingual-RP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maywell/Qwen2-7B-Multilingual-RP")
model = AutoModelForCausalLM.from_pretrained("maywell/Qwen2-7B-Multilingual-RP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maywell/Qwen2-7B-Multilingual-RP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maywell/Qwen2-7B-Multilingual-RP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Qwen2-7B-Multilingual-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maywell/Qwen2-7B-Multilingual-RP

SGLang

How to use maywell/Qwen2-7B-Multilingual-RP 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 "maywell/Qwen2-7B-Multilingual-RP" \
    --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": "maywell/Qwen2-7B-Multilingual-RP",
		"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 "maywell/Qwen2-7B-Multilingual-RP" \
        --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": "maywell/Qwen2-7B-Multilingual-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maywell/Qwen2-7B-Multilingual-RP with Docker Model Runner:
```
docker model run hf.co/maywell/Qwen2-7B-Multilingual-RP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

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Model size

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Tensor type

BF16

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