Instructions to use osunlp/PreNormLoop-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/PreNormLoop-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/PreNormLoop-0.6B", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("osunlp/PreNormLoop-0.6B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use osunlp/PreNormLoop-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/PreNormLoop-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/osunlp/PreNormLoop-0.6B

SGLang

How to use osunlp/PreNormLoop-0.6B 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 "osunlp/PreNormLoop-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "osunlp/PreNormLoop-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use osunlp/PreNormLoop-0.6B with Docker Model Runner:
```
docker model run hf.co/osunlp/PreNormLoop-0.6B
```

PreNormLoop-0.6B / config.json

ykyao

Default to BF16 weights for inference

16e089c verified 1 day ago

Raw History Blame Contribute Delete

861 Bytes

{
  "architectures": [
    "ARLoopBaselineForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_baseline.ARLoopBaselineConfig",
    "AutoModelForCausalLM": "modeling_baseline.ARLoopBaselineForCausalLM"
  },
  "bos_token_id": 0,
  "dtype": "bfloat16",
  "embedding_scale": 1.0,
  "eos_token_id": 0,
  "head_dim": 96,
  "hidden_size": 1536,
  "intermediate_size": 4608,
  "logit_softcap": null,
  "loop_count": 3,
  "max_position_embeddings": 4096,
  "model_type": "arloop_baseline",
  "num_attention_heads": 16,
  "num_hidden_layers": 54,
  "num_key_value_heads": 4,
  "pad_token_id": 0,
  "physical_layers": 18,
  "qk_norm": true,
  "rms_norm_eps": 1e-05,
  "rope_condense_ratio": 1,
  "rope_theta": 50000,
  "tie_word_embeddings": true,
  "transformers_version": "5.12.1",
  "use_cache": true,
  "vocab_size": 49152,
  "torch_dtype": "bfloat16"
}

	{
	"architectures": [
	"ARLoopBaselineForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_baseline.ARLoopBaselineConfig",
	"AutoModelForCausalLM": "modeling_baseline.ARLoopBaselineForCausalLM"
	},
	"bos_token_id": 0,
	"dtype": "bfloat16",
	"embedding_scale": 1.0,
	"eos_token_id": 0,
	"head_dim": 96,
	"hidden_size": 1536,
	"intermediate_size": 4608,
	"logit_softcap": null,
	"loop_count": 3,
	"max_position_embeddings": 4096,
	"model_type": "arloop_baseline",
	"num_attention_heads": 16,
	"num_hidden_layers": 54,
	"num_key_value_heads": 4,
	"pad_token_id": 0,
	"physical_layers": 18,
	"qk_norm": true,
	"rms_norm_eps": 1e-05,
	"rope_condense_ratio": 1,
	"rope_theta": 50000,
	"tie_word_embeddings": true,
	"transformers_version": "5.12.1",
	"use_cache": true,
	"vocab_size": 49152,
	"torch_dtype": "bfloat16"
	}