Instructions to use mehuldamani/countdown_arl-sft-add_multiply-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mehuldamani/countdown_arl-sft-add_multiply-v8 with Transformers:

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

pipe = pipeline("text-generation", model="mehuldamani/countdown_arl-sft-add_multiply-v8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mehuldamani/countdown_arl-sft-add_multiply-v8")
model = AutoModelForCausalLM.from_pretrained("mehuldamani/countdown_arl-sft-add_multiply-v8")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mehuldamani/countdown_arl-sft-add_multiply-v8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mehuldamani/countdown_arl-sft-add_multiply-v8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mehuldamani/countdown_arl-sft-add_multiply-v8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mehuldamani/countdown_arl-sft-add_multiply-v8

SGLang

How to use mehuldamani/countdown_arl-sft-add_multiply-v8 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 "mehuldamani/countdown_arl-sft-add_multiply-v8" \
    --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": "mehuldamani/countdown_arl-sft-add_multiply-v8",
		"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 "mehuldamani/countdown_arl-sft-add_multiply-v8" \
        --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": "mehuldamani/countdown_arl-sft-add_multiply-v8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mehuldamani/countdown_arl-sft-add_multiply-v8 with Docker Model Runner:
```
docker model run hf.co/mehuldamani/countdown_arl-sft-add_multiply-v8
```

countdown_arl-sft-add_multiply-v8 / model-00002-of-00003.safetensors

mehuldamani

Upload Qwen2ForCausalLM

75d5fc1 verified about 1 month ago

download

history blame contribute delete

4.93 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 67afa5a71d96cf3d29a7887a6a801b308864bd64d700d9a1bd926a3172cf56e1
Size of remote file:: 4.93 GB
SHA256:: 711f894ac37e553f9fa8815483fa92d5f56221a0ee5f3c1e6bfa3841ea350cb1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.