Instructions to use PanocularAI/PanoLM-380M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PanocularAI/PanoLM-380M with Transformers:

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

pipe = pipeline("text-generation", model="PanocularAI/PanoLM-380M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PanocularAI/PanoLM-380M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PanocularAI/PanoLM-380M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PanocularAI/PanoLM-380M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PanocularAI/PanoLM-380M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PanocularAI/PanoLM-380M

SGLang

How to use PanocularAI/PanoLM-380M 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 "PanocularAI/PanoLM-380M" \
    --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": "PanocularAI/PanoLM-380M",
		"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 "PanocularAI/PanoLM-380M" \
        --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": "PanocularAI/PanoLM-380M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PanocularAI/PanoLM-380M with Docker Model Runner:
```
docker model run hf.co/PanocularAI/PanoLM-380M
```

PanoLM-380M / config.json

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	{
	"allow_neg_eigval": false,
	"architectures": [
	"PanoLMForCausalLM"
	],
	"attn_mode": "chunk",
	"attn_variant": "kda",
	"auto_map": {
	"AutoConfig": "configuration_panolm.PanoLMConfig",
	"AutoModelForCausalLM": "modeling_panolm.PanoLMForCausalLM"
	},
	"bos_token_id": 1,
	"conv_bias": false,
	"conv_size": 4,
	"dtype": "bfloat16",
	"elementwise_affine": true,
	"eos_token_id": 2,
	"expand_ratio": 1,
	"expand_v": 1.0,
	"fuse_bitlinear": true,
	"fuse_norm": true,
	"head_dim": 128,
	"hidden_size": 1024,
	"lower_bound": -1.0,
	"max_position_embeddings": 131072,
	"mlp_hidden_dim": 2816,
	"model_type": "panolm",
	"num_attention_heads": 1,
	"num_heads": 8,
	"num_hidden_layers": 24,
	"num_reserved_token_slots": 128,
	"num_v_heads": null,
	"pad_token_id": null,
	"rms_norm_eps": 1e-05,
	"safe_gate": true,
	"tie_word_embeddings": false,
	"transformers_version": "5.8.1",
	"use_bitlinear": false,
	"use_lower_bound": false,
	"use_short_conv": true,
	"vocab_size": 32000
	}