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: 556 Bytes

586d744

{
  "architectures": [
    "PerdixForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_perdix.PerdixConfig",
    "AutoModelForCausalLM": "modeling_perdix.PerdixForCausalLM"
  },
  "bos_token_id": 0,
  "dim": 2048,
  "dtype": "float32",
  "eos_token_id": 49153,
  "ffn_dim": 8192,
  "init_std": 0.02,
  "max_seq_len": 2048,
  "model_type": "perdix",
  "n_heads": 16,
  "n_layers": 20,
  "norm_eps": 1e-05,
  "pad_token_id": 1,
  "rope_theta": 10000.0,
  "tie_word_embeddings": true,
  "transformers_version": "5.18.0",
  "vocab_size": 49154
}