Instructions to use d4g0at5h0rty/sage-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d4g0at5h0rty/sage-ml with Transformers:

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

pipe = pipeline("text-generation", model="d4g0at5h0rty/sage-ml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("d4g0at5h0rty/sage-ml", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d4g0at5h0rty/sage-ml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d4g0at5h0rty/sage-ml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d4g0at5h0rty/sage-ml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/d4g0at5h0rty/sage-ml

SGLang

How to use d4g0at5h0rty/sage-ml 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 "d4g0at5h0rty/sage-ml" \
    --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": "d4g0at5h0rty/sage-ml",
		"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 "d4g0at5h0rty/sage-ml" \
        --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": "d4g0at5h0rty/sage-ml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use d4g0at5h0rty/sage-ml with Docker Model Runner:
```
docker model run hf.co/d4g0at5h0rty/sage-ml
```

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "d4g0at5h0rty/sage-ml"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
)

gen_tokens = model.generate(
    input_ids,
    max_new_tokens=4096,
    do_sample=True,
    temperature=0.1,
    top_p=0.95
)

print(tokenizer.decode(gen_tokens[0]))

Downloads last month: 15

Safetensors

Model size

3B params

Tensor type

BF16