Instructions to use HappyBulingling/MyModel-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HappyBulingling/MyModel-sft with Transformers:

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

pipe = pipeline("text-generation", model="HappyBulingling/MyModel-sft", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HappyBulingling/MyModel-sft", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HappyBulingling/MyModel-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HappyBulingling/MyModel-sft

SGLang

How to use HappyBulingling/MyModel-sft 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 "HappyBulingling/MyModel-sft" \
    --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": "HappyBulingling/MyModel-sft",
		"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 "HappyBulingling/MyModel-sft" \
        --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": "HappyBulingling/MyModel-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HappyBulingling/MyModel-sft with Docker Model Runner:
```
docker model run hf.co/HappyBulingling/MyModel-sft
```

MyModel-sft

MyModel 稠密文本模型（sft-4k），参数量 259M，训练 step 4,968，tokens 0.17B。

使用

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "HappyBulingling/MyModel-sft"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

messages = [{"role": "user", "content": "你好"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=True
)
outputs = model.generate(
    inputs, max_new_tokens=256, do_sample=True, temperature=0.8, top_p=0.9
)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Downloads last month: 30

Safetensors

Model size

0.3B params

Tensor type

F32