Image-Text-to-Text
Transformers
Safetensors
English
Chinese
qwen3_5
qwen3.5
decision-model
structured-prediction
text-classification
conversational
Instructions to use PelaAI/KnowLine-9B-Gen1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download TRAINING_SOURCES.json from PelaAI/KnowLine-9B-Gen1: direct link, hf CLI and curl.
- Browser
- Download file 59 kB
-
https://huggingface.co/PelaAI/KnowLine-9B-Gen1/resolve/main/TRAINING_SOURCES.json
- Command line
-
hf download hf://PelaAI/KnowLine-9B-Gen1/TRAINING_SOURCES.json
-
curl -L -o TRAINING_SOURCES.json https://huggingface.co/PelaAI/KnowLine-9B-Gen1/resolve/main/TRAINING_SOURCES.json
59 kB
| { | |
| "model": "PelaAI/KnowLine-9B-Gen1", | |
| "weight_revision": "a421fc438cebe28e95de6951bc8bc440119db969", | |
| "training_rows": 1143656, | |
| "training_sha256": "9e01ff4882d28fdc350b08ebb7b70b5cb26e79c6d4d6438a43d7bdbb589ce7cd", | |
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| "additional_injection": 16824, | |
| "paired_convention_pilots": 11917 | |
| }, | |
| "inherited_source_declaration": "https://huggingface.co/PelaAI/KnowLine-31B-Gen1/blob/986803e4037f7778d24623184729a31202e3d302/README.md", | |
| "basis": "Retained mixU channel counts checked against prior source-family declarations; this is not a new record-level overlap audit. No prior models overlap counts reused. Source revisions and any undeclared families remain unknown. Pretraining exposure is unknown.", | |
| "training_data_public": false, | |
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| "oj11_community": 1944, | |
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| "kd_g_kuake_qtr": 1675, | |
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| "kd_gen_snli": 1267, | |
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| "z_finentity": 1371, | |
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| "kd_g_beavertails": 761, | |
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| "hf_nd_math_difficulty": 728, | |
| "hf_di_contractnli": 1340, | |
| "h_sgd": 1290, | |
| "hf_nd_math_subject": 711, | |
| "inj2_k_acos": 900, | |
| "z_bpomp_fmt": 905, | |
| "openjevdata_reading_comprehension": 1287, | |
| "hf_d_xnli_zh": 1186, | |
| "digap_habermas": 1178, | |
| "kd_gen_tweet_sentiment": 675, | |
| "fix7_toolret_glaive": 670, | |
| "hf_g_kuake_qtr": 1154, | |
| "kd_aes2": 657, | |
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| "blackjack": 1104, | |
| "kd_jigsaw_severity": 651, | |
| "kd_asap_sas": 647, | |
| "hf_nd_tab_bank_marketing": 645, | |
| "kd_amazon": 641, | |
| "hf_di_arc": 1103, | |
| "kd_d_cdial_bias": 639, | |
| "hf_di_newyorker": 1113, | |
| "kd_di_phish_url": 636, | |
| "web_element": 1054, | |
| "fix7_musr_ledger": 629, | |
| "kd_gen_app_reviews": 628, | |
| "kd_mj_scitail": 624, | |
| "hf_nd_stress_post": 624, | |
| "kd_cmexam": 619, | |
| "hf_di_commonsenseqa": 1089, | |
| "inj2_z_toolret": 900, | |
| "kd_mhs": 617, | |
| "kd_di_phish_email": 616, | |
| "kd_gen_amazon_polarity": 615, | |
| "hf_mj_news_huffpost": 1087, | |
| "chess_move": 1033, | |
| "fix7_musr_trip": 612, | |
| "kd_mj_scicite": 612, | |
| "kd_gen_nlpcc14_sc": 612, | |
| "kd_mj_fin_topic": 611, | |
| "kd_mj_argq_stance": 610, | |
| "kd_mj_tab_adult": 609, | |
| "hf_i_drcd_choice": 1038, | |
| "kd_mj_wdc_match": 607, | |
| "kd_d_xlwic_zh": 605, | |
| "fix7_musr_alibi": 603, | |
| "chess_theme": 1016, | |
| "kd_gen_swsr": 602, | |
| "arcade_2048": 1037, | |
| "kd_d_amz_en": 596, | |
| "kd_helpsteer": 596, | |
| "kd_gen_climate_fever": 594, | |
| "inj2_k_gsm8k": 781, | |
| "kd_gen_imdb": 592, | |
| "kd_d_bq_dev": 589, | |
| "synth_deductive@v1": 1142, | |
| "arcade_sudoku": 1052, | |
| "kd_gen_healthver": 587, | |
| "kd_patent": 587, | |
| "kd_arena140k": 585, | |
| "kd_gen_chnsenticorp": 581, | |
| "kd_d_lingnli": 578, | |
| "kd_mnli": 575, | |
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| "benchmark_id": "ruletaker-noul-test-v1", | |
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