Instructions to use yuyijiong/Qwen2-7b-Instruct-paraph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuyijiong/Qwen2-7b-Instruct-paraph with Transformers:

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

pipe = pipeline("text-generation", model="yuyijiong/Qwen2-7b-Instruct-paraph")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuyijiong/Qwen2-7b-Instruct-paraph")
model = AutoModelForCausalLM.from_pretrained("yuyijiong/Qwen2-7b-Instruct-paraph", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuyijiong/Qwen2-7b-Instruct-paraph with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yuyijiong/Qwen2-7b-Instruct-paraph

SGLang

How to use yuyijiong/Qwen2-7b-Instruct-paraph 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 "yuyijiong/Qwen2-7b-Instruct-paraph" \
    --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": "yuyijiong/Qwen2-7b-Instruct-paraph",
		"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 "yuyijiong/Qwen2-7b-Instruct-paraph" \
        --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": "yuyijiong/Qwen2-7b-Instruct-paraph",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yuyijiong/Qwen2-7b-Instruct-paraph with Docker Model Runner:
```
docker model run hf.co/yuyijiong/Qwen2-7b-Instruct-paraph
```

Qwen2-7B-Instruct模型在 Long-Instruction-with-Paraphrasing数据集上微调 1 epoch，提升了 long-context 能力

Eval on LongBench

long-context 能力得到提升

dataset	Qwen2-7B-Instruct	Qwen2-7b-Instruct-paraph
hotpotqa	42.79	49.46
dureader	24.28	33.94
multifieldqa_en	46.17	49.65
multifieldqa_zh	60.64	64.53
passage_retrieval_en	70.0	84.5
passage_retrieval_zh	56.0	70.0
trec	76.5	76.5
lsht	43.5	45.0
Average	52.48	59.20

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Dataset used to train yuyijiong/Qwen2-7b-Instruct-paraph

Collection including yuyijiong/Qwen2-7b-Instruct-paraph

train_with_paraphrasing

Collection

[long-context models trained with "original text paraphrasing" dataset](https://github.com/yuyijiong/train_with_paraphrasing) • 4 items • Updated Jun 28, 2024