Instructions to use seanphan/av-cctv-vlm-v21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use seanphan/av-cctv-vlm-v21 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "seanphan/av-cctv-vlm-v21") - Transformers
How to use seanphan/av-cctv-vlm-v21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="seanphan/av-cctv-vlm-v21") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("seanphan/av-cctv-vlm-v21", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use seanphan/av-cctv-vlm-v21 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "seanphan/av-cctv-vlm-v21" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seanphan/av-cctv-vlm-v21", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/seanphan/av-cctv-vlm-v21
- SGLang
How to use seanphan/av-cctv-vlm-v21 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 "seanphan/av-cctv-vlm-v21" \ --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": "seanphan/av-cctv-vlm-v21", "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 "seanphan/av-cctv-vlm-v21" \ --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": "seanphan/av-cctv-vlm-v21", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use seanphan/av-cctv-vlm-v21 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for seanphan/av-cctv-vlm-v21 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for seanphan/av-cctv-vlm-v21 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for seanphan/av-cctv-vlm-v21 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="seanphan/av-cctv-vlm-v21", max_seq_length=2048, ) - Docker Model Runner
How to use seanphan/av-cctv-vlm-v21 with Docker Model Runner:
docker model run hf.co/seanphan/av-cctv-vlm-v21
| # v1.0 Verification Results | |
| ## Summary | |
| | Metric | Value | Threshold | Result | | |
| |--------|-------|-----------|--------| | |
| | retrieval_similarity | 0.7239 | >= 0.60 | PASS | | |
| | tIOU | 0.6700 | >= 0.40 | PASS | | |
| | mAP@0.5 | 0.5092 | >= 0.50 | PASS | | |
| **Overall: PASS — v1.0 ACCEPTED** | |
| ## Details | |
| - Checkpoint: `outputs/checkpoint-770` (step 770, eval_loss 0.5272) | |
| - Predictions: `checkpoints/v21_semantic_boundaries/predictions.jsonl` (98 samples) | |
| - Embedder: OpenAI text-embedding-3-small (production-aligned) | |
| - Date: 2026-03-19 | |
| ### Retrieval Similarity | |
| - Mean: 0.7239 | |
| - Median: 0.7355 | |
| - Min: 0.4982, Max: 0.9162 | |
| - Excellent (>= 0.80): 22/98 | |
| - Good (>= 0.60): 87/98 | |
| - Acceptable (>= 0.40): 98/98 | |
| ### Temporal IoU (tIOU) | |
| - Mean: 0.6700 | |
| - Median: 0.6703 | |
| - Predictions with timestamps: 98/98 | |
| - References with timestamps: 98/98 | |
| ### mAP@0.5 | |
| - Mean: 0.5092 | |
| ## Production Embeddings (prior run) | |
| For reference, the prior eval with OpenAI text-embedding-3-small on 20 pairs showed: | |
| - mean_similarity: 0.6781 | |
| - production_ready: true | |
| ## Failure Analysis | |
| All metrics pass. v1.0 is accepted for production. | |
| ## Per-Sample Scores (first 10) | |
| | # | retrieval_sim | tIOU | mAP@0.5 | pred_events | ref_events | | |
| |---|--------------|------|---------|-------------|------------| | |
| | 0 | 0.6549 | 0.5277 | 0.4500 | 5 | 4 | | |
| | 1 | 0.7739 | 0.8080 | 1.0000 | 5 | 5 | | |
| | 2 | 0.6433 | 1.0000 | 1.0000 | 4 | 4 | | |
| | 3 | 0.7473 | 0.6228 | 0.4500 | 5 | 4 | | |
| | 4 | 0.7774 | 0.5406 | 0.2500 | 4 | 4 | | |
| | 5 | 0.7924 | 0.3868 | 0.0500 | 4 | 5 | | |
| | 6 | 0.7626 | 0.7779 | 0.7500 | 4 | 3 | | |
| | 7 | 0.6476 | 0.8060 | 0.7500 | 4 | 3 | | |
| | 8 | 0.7355 | 0.7520 | 0.6400 | 5 | 5 | | |
| | 9 | 0.8859 | 0.6579 | 0.5625 | 4 | 4 | | |