Instructions to use Haicaochi/Qwen_05_txtt_V2_Stable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Haicaochi/Qwen_05_txtt_V2_Stable with Transformers:

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

pipe = pipeline("text-generation", model="Haicaochi/Qwen_05_txtt_V2_Stable")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Haicaochi/Qwen_05_txtt_V2_Stable")
model = AutoModelForCausalLM.from_pretrained("Haicaochi/Qwen_05_txtt_V2_Stable")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Haicaochi/Qwen_05_txtt_V2_Stable with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Haicaochi/Qwen_05_txtt_V2_Stable"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haicaochi/Qwen_05_txtt_V2_Stable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Haicaochi/Qwen_05_txtt_V2_Stable

SGLang

How to use Haicaochi/Qwen_05_txtt_V2_Stable 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 "Haicaochi/Qwen_05_txtt_V2_Stable" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haicaochi/Qwen_05_txtt_V2_Stable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Haicaochi/Qwen_05_txtt_V2_Stable" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haicaochi/Qwen_05_txtt_V2_Stable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Haicaochi/Qwen_05_txtt_V2_Stable with Docker Model Runner:
```
docker model run hf.co/Haicaochi/Qwen_05_txtt_V2_Stable
```

Qwen_05_txtt_V2_Stable

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3150

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-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss
0.6787	0.96	3	0.4154
0.6787	1.92	6	0.3058
0.6787	2.88	9	0.2565
0.3614	3.84	12	0.2357
0.3614	4.8	15	0.2368
0.3614	5.76	18	0.2444
0.1194	6.72	21	0.2615
0.1194	8.0	25	0.2914
0.1194	8.96	28	0.3150

Framework versions

Transformers 4.44.2
Pytorch 2.9.0+cu128
Datasets 2.14.6
Tokenizers 0.19.1

Downloads last month: 15

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for Haicaochi/Qwen_05_txtt_V2_Stable

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(899)

this model