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

Libraries

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

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

pipe = pipeline("text-generation", model="lightblue/reranker_0.5_bincont_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_bincont_filt")
model = AutoModelForCausalLM.from_pretrained("lightblue/reranker_0.5_bincont_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_bincont_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_bincont_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_bincont_filt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

reranker_bincont_filt_train

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

Loss: 0.1613

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
0.209	0.1000	3952	0.2207
0.1541	0.2000	7904	0.2108
0.1519	0.3000	11856	0.2030
0.3499	0.4000	15808	0.1939
0.1045	0.5000	19760	0.1834
0.1887	0.6000	23712	0.1770
0.2182	0.7001	27664	0.1695
0.1281	0.8001	31616	0.1645
0.1463	0.9001	35568	0.1617

Framework versions

Transformers 4.46.1
Pytorch 2.4.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 2

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for lightblue/reranker_0.5_bincont_filt

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(793)

this model