Instructions to use lightblue/reranker_0.5_bin_filt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lightblue/reranker_0.5_bin_filt with Transformers:

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

pipe = pipeline("text-generation", model="lightblue/reranker_0.5_bin_filt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lightblue/reranker_0.5_bin_filt")
model = AutoModelForCausalLM.from_pretrained("lightblue/reranker_0.5_bin_filt")
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 lightblue/reranker_0.5_bin_filt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lightblue/reranker_0.5_bin_filt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightblue/reranker_0.5_bin_filt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lightblue/reranker_0.5_bin_filt

SGLang

How to use lightblue/reranker_0.5_bin_filt 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 "lightblue/reranker_0.5_bin_filt" \
    --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": "lightblue/reranker_0.5_bin_filt",
		"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 "lightblue/reranker_0.5_bin_filt" \
        --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": "lightblue/reranker_0.5_bin_filt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lightblue/reranker_0.5_bin_filt with Docker Model Runner:
```
docker model run hf.co/lightblue/reranker_0.5_bin_filt
```

reranker_binary_filt_train

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

Loss: 0.0526

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
total_train_batch_size: 8
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: 0.01
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.0517	0.1000	1937	0.0871
0.114	0.2001	3874	0.0835
0.1033	0.3001	5811	0.0735
0.0544	0.4001	7748	0.0663
0.1169	0.5001	9685	0.0623
0.05	0.6002	11622	0.0599
0.0951	0.7002	13559	0.0566
0.0497	0.8002	15496	0.0551
0.1002	0.9002	17433	0.0532

Framework versions

Transformers 4.46.1
Pytorch 2.4.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 6

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for lightblue/reranker_0.5_bin_filt

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(759)

this model