Instructions to use DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt with Transformers:

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

pipe = pipeline("text-generation", model="DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt

SGLang

How to use DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt 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 "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt" \
    --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": "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt",
		"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 "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt" \
        --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": "DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt with Docker Model Runner:
```
docker model run hf.co/DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt
```

qwen2_chunking_mlp_freeze_uniform_with_shared_start_sft

This model is a fine-tuned version of DongfuJiang/Qwen2.5-0.5B-Instruct on the wikitext-103-v1 dataset. It achieves the following results on the evaluation set:

Loss: 3.9502

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-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
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: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
4.107	0.5867	500	4.0980

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 2.18.0
Tokenizers 0.20.3

Downloads last month: 5

Safetensors

Model size

0.5B params

Tensor type

F32

BF16

Model tree for DongfuJiang/qwen2_chunking_mlp_freeze_uniform_with_shared_start_pt

Base model

Qwen/Qwen2.5-0.5B

Finetuned

DongfuJiang/Qwen2.5-0.5B-Instruct

Finetuned

(3)

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