Instructions to use studyinglover/IntelliKernel-0.03b-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studyinglover/IntelliKernel-0.03b-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="studyinglover/IntelliKernel-0.03b-pretrained", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("studyinglover/IntelliKernel-0.03b-pretrained", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use studyinglover/IntelliKernel-0.03b-pretrained with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "studyinglover/IntelliKernel-0.03b-pretrained" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "studyinglover/IntelliKernel-0.03b-pretrained", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/studyinglover/IntelliKernel-0.03b-pretrained
- SGLang
How to use studyinglover/IntelliKernel-0.03b-pretrained 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 "studyinglover/IntelliKernel-0.03b-pretrained" \ --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": "studyinglover/IntelliKernel-0.03b-pretrained", "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 "studyinglover/IntelliKernel-0.03b-pretrained" \ --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": "studyinglover/IntelliKernel-0.03b-pretrained", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use studyinglover/IntelliKernel-0.03b-pretrained with Docker Model Runner:
docker model run hf.co/studyinglover/IntelliKernel-0.03b-pretrained
Download config.json from studyinglover/IntelliKernel-0.03b-pretrained: direct link, hf CLI and curl.
- Browser
- Download file 515 Bytes
-
https://huggingface.co/studyinglover/IntelliKernel-0.03b-pretrained/resolve/main/config.json
- Command line
-
hf download hf://studyinglover/IntelliKernel-0.03b-pretrained/config.json
-
curl -L -o config.json https://huggingface.co/studyinglover/IntelliKernel-0.03b-pretrained/resolve/main/config.json
515 Bytes
| { | |
| "_name_or_path": "output/pretrain_model_hf", | |
| "architectures": [ | |
| "Transformer" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "config.ModelConfig", | |
| "AutoModelForCausalLM": "model.Transformer" | |
| }, | |
| "dim": 512, | |
| "dropout": 0.0, | |
| "flash_attn": true, | |
| "hidden_dim": null, | |
| "max_seq_len": 512, | |
| "model_type": "IntelliKernel", | |
| "multiple_of": 64, | |
| "n_heads": 16, | |
| "n_kv_heads": 8, | |
| "n_layers": 8, | |
| "norm_eps": 1e-05, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.0", | |
| "vocab_size": 6400 | |
| } | |