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 / tokenizer_config.json

prismdata

Perdix-1.1B-Instruct: SFT of Perdix-1.1B-Base

586d744 verified 1 day ago

Raw History Blame Contribute Delete

510 Bytes

	{
	"bos_token": "<\|endoftext\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"model_max_length": 2048,
	"pad_token": "<\|pad\|>",
	"tokenizer_class": "PreTrainedTokenizerFast",
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>"
	],
	"chat_template": "{% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\\n' + message['content'] \| trim + '<\|im_end\|>\\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\\n' }}{% endif %}"
	}

	{
	"bos_token": "<\|endoftext\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"model_max_length": 2048,
	"pad_token": "<\|pad\|>",
	"tokenizer_class": "PreTrainedTokenizerFast",
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>"
	],
	"chat_template": "{% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\\n' + message['content'] \| trim + '<\|im_end\|>\\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\\n' }}{% endif %}"
	}