Instructions to use inclusionAI/Ling-Coder-lite-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-Coder-lite-base with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-Coder-lite-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-Coder-lite-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-Coder-lite-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-Coder-lite-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-Coder-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-Coder-lite-base

SGLang

How to use inclusionAI/Ling-Coder-lite-base 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 "inclusionAI/Ling-Coder-lite-base" \
    --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": "inclusionAI/Ling-Coder-lite-base",
		"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 "inclusionAI/Ling-Coder-lite-base" \
        --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": "inclusionAI/Ling-Coder-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inclusionAI/Ling-Coder-lite-base with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-Coder-lite-base
```

Ling-Coder-lite-base

File size: 631 Bytes

744618f

{
    "add_bos_token": false,
    "add_eos_token": false,
    "additional_special_tokens": [
        "<|fim_begin|>",
        "<|fim_hole|>",
        "<|fim_end|>",
        "<|fim_pad|>",
        "<|repo_name|>",
        "<|file_sep|>"
    ],
    "bos_token": "<|startoftext|>",
    "clean_up_tokenization_spaces": false,
    "cls_token": "[CLS]",
    "eos_token": "<|endoftext|>",
    "gmask_token": "[gMASK]",
    "merges_file": null,
    "model_max_length": 1000000000000000019884624838656,
    "tokenizer_class": "PreTrainedTokenizerFast",
    "vocab_file": null,
    "pad_token": "<|endoftext|>",
    "fast_tokenizer": true
}