Instructions to use AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1 with Transformers:

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

pipe = pipeline("text-generation", model="AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1

SGLang

How to use AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1 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 "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1" \
    --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": "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1",
		"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 "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1" \
        --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": "AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1
```

See axolotl config

axolotl version: 0.8.1

base_model: AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel
model_type: AutoModelForCausalLM
trust_remote_code: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: /mnt/shared/twsc/alex/qwen/s1_claude.jsonl
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles_to_train:
    train_on_eos:

dataset_prepared_path: ./sft_dataprep/
val_set_size: 0
output_dir: ./placeholder_sft/
shuffle_merged_datasets: false

sequence_len: 20000
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: Reasoning_TP1_2025
wandb_entity:
wandb_watch:
wandb_name: Qwen-2.5-Omni-Reasoning-250422_sft
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1.0

adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-8

bf16: true
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
logging_steps: 1
flash_attention: true
xformers_attention: false
sdp_attention: false

warmup_ratio: 0.05
saves_per_epoch: 1
save_total_limit: 5
weight_decay: 0.1
deepspeed: /mnt/shared/twsc/alex/reasoning/zero3_bf16.json
special_tokens:
  pad_token: "<pad>"
tokens:
  - <think>
  - </think>
seed: 42

placeholder_sft/

This model is a fine-tuned version of AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel on the /mnt/shared/twsc/alex/qwen/s1_claude.jsonl dataset.

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: 32
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3
num_epochs: 5.0

Training results

Framework versions

Transformers 4.52.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 26

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1

Base model

AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel

Finetuned

(2)

this model

AlexHung29629
/

Qwen-2.5-Omni-ThinkerTextModel-s1

placeholder_sft/

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1

Dataset used to train AlexHung29629/Qwen-2.5-Omni-ThinkerTextModel-s1