Instructions to use PelaAI/KnowLine-9B-Gen1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PelaAI/KnowLine-9B-Gen1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="PelaAI/KnowLine-9B-Gen1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PelaAI/KnowLine-9B-Gen1")
model = AutoModelForMultimodalLM.from_pretrained("PelaAI/KnowLine-9B-Gen1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PelaAI/KnowLine-9B-Gen1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PelaAI/KnowLine-9B-Gen1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PelaAI/KnowLine-9B-Gen1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/PelaAI/KnowLine-9B-Gen1

SGLang

How to use PelaAI/KnowLine-9B-Gen1 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 "PelaAI/KnowLine-9B-Gen1" \
    --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": "PelaAI/KnowLine-9B-Gen1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "PelaAI/KnowLine-9B-Gen1" \
        --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": "PelaAI/KnowLine-9B-Gen1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use PelaAI/KnowLine-9B-Gen1 with Docker Model Runner:
```
docker model run hf.co/PelaAI/KnowLine-9B-Gen1
```

KnowLine-9B-Gen1 / SHA256SUMS

PEScn

Document retained mixU source-family exposure for S1MB

4083a79 verified 10 days ago

Raw History Blame Contribute Delete

2.24 kB

93691240311786c93e1c21b53dfb9d52743cf02958709c1048fab77760aacb73  CALIBRATION.json
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855  FIXED
39880bb24c44aea652a15eb8d1908f5ee85f0ad303a7513382382aaf5f9c9a37  INFERENCE.md
50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32  LICENSE
ab25a1399625bdaa46d8cd31bf805f4a56e7a3254af7ef1f1e94a02bcf6a1a1c  README.md
b5bd4c94126b15f6c158c75909b0361f8019e05c7d7e6c76a8d81d8b64933615  README.zh.md
a210ce25dadbcc9a920e486c832f78c27f9ab6102a89010be4f657ec18679a48  TRAINING_SOURCES.json
a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715  chat_template.jinja
d0883072e01861ed0b2d47be3c16c36a8e81c224c7ffaa310c6558fb3f932b05  config.json
2d4464e2ead06bc9bc718c781309ad1e7baded626d66e8dcdc8b469ba185faf0  configuration.json
4f05e06b6021e6b0b5f00d4c6bc2f98a3a824c9a254ec901ba42c9070ac50ac9  knowline_engine.py
2e839fa24f1ad9c980898b2a9aa62e2154ba3f57618bd5f2fedb5756f61f062f  knowline_layout.py
13ca50c4001fac0aa3899a35ff01e60f311132c571b17a8c3dc7aa6001ad2f3f  knowline_server.py
a9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d  merges.txt
fe50afcf194c87e6faaf5c89ef74caf4f9e635f70ca8a5e8b14231383415d13d  model-00001-of-00004.safetensors
4981e1d31be80bb4bacfaa57b0ad9c3370ce170eee5f71451229017b5a52a2f7  model-00002-of-00004.safetensors
f901558f89699dc6d96343ba9cdb29c916d0db1377c4ecfa546f2d280cc5373a  model-00003-of-00004.safetensors
1740ac4322df8d622829c34cb92ac2619d5ec2ae2f703e7f8c6dcaf0567cf882  model-00004-of-00004.safetensors
c41ed69b9d21f0772737d4fb74bf48283a473ec34fb5df07afb487f35d67c28f  model-extra-from-base.safetensors
efee6aea7d27319549e35f3bf76600eeb1313fa6bedf353d810e053bbdff8a81  model.safetensors.index.json
27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516  preprocessor_config.json
401aa4b029b48967633807a577767448ac937c3c35d43b598d6c042982ba70b4  serve_knowline.sh
5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42  tokenizer.json
316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8  tokenizer_config.json
7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13  video_preprocessor_config.json
ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003  vocab.json

	93691240311786c93e1c21b53dfb9d52743cf02958709c1048fab77760aacb73 CALIBRATION.json
	e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 FIXED
	39880bb24c44aea652a15eb8d1908f5ee85f0ad303a7513382382aaf5f9c9a37 INFERENCE.md
	50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32 LICENSE
	ab25a1399625bdaa46d8cd31bf805f4a56e7a3254af7ef1f1e94a02bcf6a1a1c README.md
	b5bd4c94126b15f6c158c75909b0361f8019e05c7d7e6c76a8d81d8b64933615 README.zh.md
	a210ce25dadbcc9a920e486c832f78c27f9ab6102a89010be4f657ec18679a48 TRAINING_SOURCES.json
	a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715 chat_template.jinja
	d0883072e01861ed0b2d47be3c16c36a8e81c224c7ffaa310c6558fb3f932b05 config.json
	2d4464e2ead06bc9bc718c781309ad1e7baded626d66e8dcdc8b469ba185faf0 configuration.json
	4f05e06b6021e6b0b5f00d4c6bc2f98a3a824c9a254ec901ba42c9070ac50ac9 knowline_engine.py
	2e839fa24f1ad9c980898b2a9aa62e2154ba3f57618bd5f2fedb5756f61f062f knowline_layout.py
	13ca50c4001fac0aa3899a35ff01e60f311132c571b17a8c3dc7aa6001ad2f3f knowline_server.py
	a9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d merges.txt
	fe50afcf194c87e6faaf5c89ef74caf4f9e635f70ca8a5e8b14231383415d13d model-00001-of-00004.safetensors
	4981e1d31be80bb4bacfaa57b0ad9c3370ce170eee5f71451229017b5a52a2f7 model-00002-of-00004.safetensors
	f901558f89699dc6d96343ba9cdb29c916d0db1377c4ecfa546f2d280cc5373a model-00003-of-00004.safetensors
	1740ac4322df8d622829c34cb92ac2619d5ec2ae2f703e7f8c6dcaf0567cf882 model-00004-of-00004.safetensors
	c41ed69b9d21f0772737d4fb74bf48283a473ec34fb5df07afb487f35d67c28f model-extra-from-base.safetensors
	efee6aea7d27319549e35f3bf76600eeb1313fa6bedf353d810e053bbdff8a81 model.safetensors.index.json
	27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516 preprocessor_config.json
	401aa4b029b48967633807a577767448ac937c3c35d43b598d6c042982ba70b4 serve_knowline.sh
	5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42 tokenizer.json
	316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8 tokenizer_config.json
	7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13 video_preprocessor_config.json
	ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003 vocab.json