Instructions to use spandyie/amadablam-322m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spandyie/amadablam-322m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="spandyie/amadablam-322m-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("spandyie/amadablam-322m-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use spandyie/amadablam-322m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spandyie/amadablam-322m-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": "spandyie/amadablam-322m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/spandyie/amadablam-322m-instruct

SGLang

How to use spandyie/amadablam-322m-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 "spandyie/amadablam-322m-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": "spandyie/amadablam-322m-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 "spandyie/amadablam-322m-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": "spandyie/amadablam-322m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use spandyie/amadablam-322m-instruct with Docker Model Runner:
```
docker model run hf.co/spandyie/amadablam-322m-instruct
```

amadablam-322m-instruct / config.json

spandyie

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	{
	"architectures": [
	"AmadablamForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_amadablam.AmadablamConfig",
	"AutoModelForCausalLM": "modeling_amadablam.AmadablamForCausalLM"
	},
	"dense_every": 8,
	"dtype": "float32",
	"hidden_size": 1280,
	"linear_chunk": 128,
	"linear_decay": 0.7,
	"logit_softcap": 15.0,
	"max_position_embeddings": 8192,
	"model_type": "amadablam",
	"num_attention_heads": 10,
	"num_hidden_layers": 14,
	"num_key_value_heads": 5,
	"rope_theta": 10000.0,
	"tie_word_embeddings": false,
	"transformers_version": "5.14.1",
	"use_cache": false,
	"vocab_size": 16384
	}