Instructions to use abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6 with Transformers:

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

pipe = pipeline("text-generation", model="abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6")
model = AutoModelForCausalLM.from_pretrained("abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6", device_map="auto")
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 Settings

vLLM

How to use abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

SGLang

How to use abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6 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 "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6" \
    --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": "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6",
		"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 "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6" \
        --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": "abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6 with Docker Model Runner:
```
docker model run hf.co/abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6
```

See axolotl config

axolotl version: 0.12.2

base_model: Qwen/Qwen3-0.6B

# Automatically upload checkpoint and final model to HF
hub_model_id: abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: qwen3
datasets:
  - path: kaggle-map/listwise-rerank
    type: chat_template
    split: train

test_datasets:
  - path: kaggle-map/listwise-rerank
    type: chat_template
    split: val

streaming: true

dataset_processes: 32
dataset_prepared_path: last_run_prepared
output_dir: ./outputs/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

sequence_len: 1280
sample_packing: true
eval_sample_packing: false

deepspeed: deepspeed_configs/zero1.json

wandb_project: map-math-misconceptions
wandb_entity:
wandb_watch:
wandb_name: listwise-rerank-qwen3-600m-ds1-9fh4jd8e6
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 16
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 5e-6

bf16: true
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 10
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 21
saves_per_epoch: 21
weight_decay: 0.01

save_first_step: true

listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the kaggle-map/listwise-rerank dataset. It achieves the following results on the evaluation set:

Loss: 0.1884
Memory/max Mem Active(gib): 43.13
Memory/max Mem Allocated(gib): 43.13
Memory/device Mem Reserved(gib): 50.69

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: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 512
total_eval_batch_size: 64
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 24
training_steps: 248

Training results

Training Loss	Epoch	Step	Validation Loss	Mem Active(gib)	Mem Allocated(gib)	Mem Reserved(gib)
No log	0	0	10.3241	31.76	31.76	32.02
7.3879	0.0483	12	0.9112	43.13	43.13	50.69
0.716	0.0967	24	0.4508	43.13	43.13	50.69
0.4393	0.1450	36	0.3112	43.13	43.13	50.69
0.3042	0.1934	48	0.2664	43.13	43.13	50.69
0.2363	0.2417	60	0.2359	43.13	43.13	50.69
0.1976	0.2900	72	0.2041	43.13	43.13	50.69
0.1743	0.3384	84	0.1951	43.13	43.13	50.69
0.161	0.3867	96	0.1836	43.13	43.13	50.69
0.1528	0.4350	108	0.1788	43.13	43.13	50.69
0.1367	0.4834	120	0.1721	43.13	43.13	50.69
0.1169	0.5317	132	0.1740	43.13	43.13	50.69
0.1136	0.5801	144	0.1701	43.13	43.13	50.69
0.1066	0.6284	156	0.1699	43.13	43.13	50.69
0.1079	0.6767	168	0.1811	43.13	43.13	50.69
0.0897	0.7251	180	0.1827	43.13	43.13	50.69
0.0883	0.7734	192	0.1869	43.13	43.13	50.69
0.0818	0.8218	204	0.1847	43.13	43.13	50.69
0.0807	0.8701	216	0.1859	43.13	43.13	50.69
0.0764	0.9184	228	0.1873	43.13	43.13	50.69
0.0722	0.9668	240	0.1884	43.13	43.13	50.69

Framework versions

Transformers 4.55.2
Pytorch 2.6.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 15

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1275)

this model

abdullahmeda
/

listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

Dataset used to train abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6