Instructions to use prismdata/Perdix-1.1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prismdata/Perdix-1.1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="prismdata/Perdix-1.1B-Instruct", 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("prismdata/Perdix-1.1B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prismdata/Perdix-1.1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prismdata/Perdix-1.1B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prismdata/Perdix-1.1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prismdata/Perdix-1.1B-Instruct

SGLang

How to use prismdata/Perdix-1.1B-Instruct 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 "prismdata/Perdix-1.1B-Instruct" \
    --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": "prismdata/Perdix-1.1B-Instruct",
		"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 "prismdata/Perdix-1.1B-Instruct" \
        --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": "prismdata/Perdix-1.1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prismdata/Perdix-1.1B-Instruct with Docker Model Runner:
```
docker model run hf.co/prismdata/Perdix-1.1B-Instruct
```

Perdix-1.1B-Instruct

File size: 1,294 Bytes

586d744

"""Perdix 설정 (transformers 연동용)."""
from transformers import PretrainedConfig


class PerdixConfig(PretrainedConfig):
    model_type = "perdix"
    attribute_map = {"hidden_size": "dim", "num_hidden_layers": "n_layers",
                     "num_attention_heads": "n_heads",
                     "max_position_embeddings": "max_seq_len"}

    def __init__(self, vocab_size=49152, dim=2048, n_layers=20, n_heads=16,
                 ffn_dim=8192, max_seq_len=2048, rope_theta=10000.0,
                 norm_eps=1e-5, init_std=0.02, bos_token_id=0, eos_token_id=0,
                 pad_token_id=1, tie_word_embeddings=True, **kwargs):
        self.vocab_size = vocab_size
        self.dim = dim
        self.n_layers = n_layers
        self.n_heads = n_heads          # differential attention은 head_dim을 반으로 나눠 2쌍 사용
        self.ffn_dim = ffn_dim
        self.max_seq_len = max_seq_len
        self.rope_theta = rope_theta
        self.norm_eps = norm_eps
        self.init_std = init_std
        kwargs.setdefault("use_cache", False)  # KV 캐시 미구현
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id,
                         pad_token_id=pad_token_id,
                         tie_word_embeddings=tie_word_embeddings, **kwargs)