Instructions to use studyinglover/IntelliKernel-0.03b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use studyinglover/IntelliKernel-0.03b-sft with Transformers:

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

pipe = pipeline("text-generation", model="studyinglover/IntelliKernel-0.03b-sft", 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-sft", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use studyinglover/IntelliKernel-0.03b-sft 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-sft"
# 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-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/studyinglover/IntelliKernel-0.03b-sft

SGLang

How to use studyinglover/IntelliKernel-0.03b-sft 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-sft" \
    --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-sft",
		"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-sft" \
        --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-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use studyinglover/IntelliKernel-0.03b-sft with Docker Model Runner:
```
docker model run hf.co/studyinglover/IntelliKernel-0.03b-sft
```

IntelliKernel-0.03b-sft / config.py

studyinglover

Upload Transformer

6dc6ea5 verified about 2 years ago

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from transformers import PretrainedConfig


class ModelConfig(PretrainedConfig):
    model_type = "IntelliKernel"

    def __init__(
        self,
        dim: int = 512,
        n_layers: int = 8,
        n_heads: int = 16,
        n_kv_heads: int = 8,
        vocab_size: int = 6400,
        hidden_dim: int = None,
        multiple_of: int = 64,
        norm_eps: float = 1e-5,
        max_seq_len: int = 512,
        dropout: float = 0.0,
        flash_attn: bool = True,
        **kwargs,
    ):
        self.dim = dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.multiple_of = multiple_of
        self.norm_eps = norm_eps
        self.max_seq_len = max_seq_len
        self.dropout = dropout
        self.flash_attn = flash_attn

        super().__init__(**kwargs)

	from transformers import PretrainedConfig


	class ModelConfig(PretrainedConfig):
	model_type = "IntelliKernel"

	def __init__(
	self,
	dim: int = 512,
	n_layers: int = 8,
	n_heads: int = 16,
	n_kv_heads: int = 8,
	vocab_size: int = 6400,
	hidden_dim: int = None,
	multiple_of: int = 64,
	norm_eps: float = 1e-5,
	max_seq_len: int = 512,
	dropout: float = 0.0,
	flash_attn: bool = True,
	**kwargs,
	):
	self.dim = dim
	self.n_layers = n_layers
	self.n_heads = n_heads
	self.n_kv_heads = n_kv_heads
	self.vocab_size = vocab_size
	self.hidden_dim = hidden_dim
	self.multiple_of = multiple_of
	self.norm_eps = norm_eps
	self.max_seq_len = max_seq_len
	self.dropout = dropout
	self.flash_attn = flash_attn

	super().__init__(**kwargs)