Instructions to use LnL-AI/opt-125M-autoround-lm_head-true-symTrue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LnL-AI/opt-125M-autoround-lm_head-true-symTrue with Transformers:

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

pipe = pipeline("text-generation", model="LnL-AI/opt-125M-autoround-lm_head-true-symTrue")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LnL-AI/opt-125M-autoround-lm_head-true-symTrue")
model = AutoModelForCausalLM.from_pretrained("LnL-AI/opt-125M-autoround-lm_head-true-symTrue", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LnL-AI/opt-125M-autoround-lm_head-true-symTrue with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LnL-AI/opt-125M-autoround-lm_head-true-symTrue"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LnL-AI/opt-125M-autoround-lm_head-true-symTrue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LnL-AI/opt-125M-autoround-lm_head-true-symTrue

SGLang

How to use LnL-AI/opt-125M-autoround-lm_head-true-symTrue 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 "LnL-AI/opt-125M-autoround-lm_head-true-symTrue" \
    --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": "LnL-AI/opt-125M-autoround-lm_head-true-symTrue",
		"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 "LnL-AI/opt-125M-autoround-lm_head-true-symTrue" \
        --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": "LnL-AI/opt-125M-autoround-lm_head-true-symTrue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LnL-AI/opt-125M-autoround-lm_head-true-symTrue with Docker Model Runner:
```
docker model run hf.co/LnL-AI/opt-125M-autoround-lm_head-true-symTrue
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 python3 main.py \
--model_name facebook/opt-125M \
--device 0 \
--group_size 128 \
--bits 4 \
--seqlen 2048 \
--iters 1000 \
--use_quant_input \
--quant_lm_head \
--disable_eval \
--n_blocks 22 \
--sym \
--deployment_device 'gpu' \
--disable_low_gpu_mem_usage \
--output_dir "/monster/data/zx/opt-125M-quant_lm_head_true"

quant model path:

/monster/data/zx/opt-125M-quant_lm_head_true/opt-125m-autoround-w4g128-gpu

Downloads last month: 17

Safetensors

Model size

0.2B params

Tensor type

I32

F16