Instructions to use CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct", 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 CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct

SGLang

How to use CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct 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 "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct" \
    --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": "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct",
		"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 "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct" \
        --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": "CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

reasoning_test

It achieves the following results on the evaluation set:

Loss: 0.1194

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: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
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: 0.1
num_epochs: 25.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.0614	3.5039	25	0.0718
0.0119	7.1260	50	0.0773
0.0025	10.6299	75	0.0889
0.0006	14.2520	100	0.1082
0.0002	17.7559	125	0.1155
0.0002	21.3780	150	0.1182
0.0001	24.8819	175	0.1194

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 7

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for CAS-SIAT-XinHai/MeasHalu-0.5B-Instruct

Quantizations

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