Instructions to use reasonrag/Qwen2.5-7B-Instruct-ReasonRAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reasonrag/Qwen2.5-7B-Instruct-ReasonRAG with Transformers:

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

pipe = pipeline("text-generation", model="reasonrag/Qwen2.5-7B-Instruct-ReasonRAG")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reasonrag/Qwen2.5-7B-Instruct-ReasonRAG")
model = AutoModelForCausalLM.from_pretrained("reasonrag/Qwen2.5-7B-Instruct-ReasonRAG")
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 Settings

vLLM

How to use reasonrag/Qwen2.5-7B-Instruct-ReasonRAG with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reasonrag/Qwen2.5-7B-Instruct-ReasonRAG

SGLang

How to use reasonrag/Qwen2.5-7B-Instruct-ReasonRAG 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 "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG" \
    --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": "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG",
		"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 "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG" \
        --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": "reasonrag/Qwen2.5-7B-Instruct-ReasonRAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reasonrag/Qwen2.5-7B-Instruct-ReasonRAG with Docker Model Runner:
```
docker model run hf.co/reasonrag/Qwen2.5-7B-Instruct-ReasonRAG
```

Qwen2.5-7B-Instruct-ReasonRAG / README.md

Wenlin Zhang

Upload README.md

e400b7c verified about 1 year ago

preview code

Raw

History Blame Contribute Delete

2.4 kB

	---
	library_name: transformers
	license: other
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- llama-factory
	- full
	- generated_from_trainer
	model-index:
	- name: dpo_v16
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# dpo_v16

	This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the dpo_mcts_rag_v8 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8564
	- Rewards/chosen: 1.0146
	- Rewards/rejected: -0.5767
	- Rewards/accuracies: 0.6204
	- Rewards/margins: 1.5913
	- Logps/chosen: -65.8051
	- Logps/rejected: -74.8917
	- Logits/chosen: -0.5108
	- Logits/rejected: -0.5206

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1e-06
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 3
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 12
	- total_eval_batch_size: 3
	- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.2
	- num_epochs: 1.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/chosen \| Logps/rejected \| Logits/chosen \| Logits/rejected \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:------------:\|:--------------:\|:-------------:\|:---------------:\|
	\| 0.9257 \| 0.4352 \| 500 \| 0.9691 \| 1.0721 \| -0.2211 \| 0.6171 \| 1.2933 \| -65.6133 \| -73.7064 \| -0.5575 \| -0.5659 \|
	\| 0.7847 \| 0.8703 \| 1000 \| 0.8689 \| 0.7341 \| -0.9130 \| 0.6223 \| 1.6471 \| -66.7400 \| -76.0126 \| -0.5218 \| -0.5319 \|


	### Framework versions

	- Transformers 4.49.0
	- Pytorch 2.5.1+cu124
	- Datasets 3.2.0
	- Tokenizers 0.21.0