Instructions to use w3en2g/qa-Qwen2.5-32B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use w3en2g/qa-Qwen2.5-32B-Base with Transformers:

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

pipe = pipeline("text-generation", model="w3en2g/qa-Qwen2.5-32B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("w3en2g/qa-Qwen2.5-32B-Base")
model = AutoModelForCausalLM.from_pretrained("w3en2g/qa-Qwen2.5-32B-Base")
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

vLLM

How to use w3en2g/qa-Qwen2.5-32B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "w3en2g/qa-Qwen2.5-32B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "w3en2g/qa-Qwen2.5-32B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/w3en2g/qa-Qwen2.5-32B-Base

SGLang

How to use w3en2g/qa-Qwen2.5-32B-Base 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 "w3en2g/qa-Qwen2.5-32B-Base" \
    --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": "w3en2g/qa-Qwen2.5-32B-Base",
		"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 "w3en2g/qa-Qwen2.5-32B-Base" \
        --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": "w3en2g/qa-Qwen2.5-32B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use w3en2g/qa-Qwen2.5-32B-Base with Docker Model Runner:
```
docker model run hf.co/w3en2g/qa-Qwen2.5-32B-Base
```

Qwen2.5-32B-Base

This model is a fine-tuned version of Qwen/Qwen2.5-32B on the QA_train_data dataset. It achieves the following results on the evaluation set:

Loss: 0.9999

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: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 16
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Use OptimizerNames.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: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.9373	0.1778	100	0.9672
0.9671	0.3556	200	0.9639
0.9584	0.5333	300	0.9629
0.957	0.7111	400	0.9597
0.9477	0.8889	500	0.9587
0.8552	1.0667	600	0.9710
0.7944	1.2444	700	0.9722
0.7359	1.4222	800	0.9709
0.8494	1.6	900	0.9662
0.8163	1.7778	1000	0.9663
0.8041	1.9556	1100	0.9639
0.6291	2.1333	1200	0.9990
0.6122	2.3111	1300	1.0004
0.6718	2.4889	1400	1.0003
0.6712	2.6667	1500	1.0002
0.6397	2.8444	1600	0.9996

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 2.21.0
Tokenizers 0.20.3

Downloads last month: 1

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for w3en2g/qa-Qwen2.5-32B-Base

Base model

Qwen/Qwen2.5-32B

Finetuned

(122)

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