Instructions to use chen-l/LiveMem-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chen-l/LiveMem-RL with Transformers:

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

pipe = pipeline("text-generation", model="chen-l/LiveMem-RL", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("chen-l/LiveMem-RL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chen-l/LiveMem-RL with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/chen-l/LiveMem-RL

SGLang

How to use chen-l/LiveMem-RL 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 "chen-l/LiveMem-RL" \
    --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": "chen-l/LiveMem-RL",
		"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 "chen-l/LiveMem-RL" \
        --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": "chen-l/LiveMem-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chen-l/LiveMem-RL with Docker Model Runner:
```
docker model run hf.co/chen-l/LiveMem-RL
```

LiveMem-RL

File size: 2,039 Bytes

3402dca

{
  "architectures": [
    "LiveMemForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_livemem.LiveMemConfig",
    "AutoModel": "modeling_livemem.LiveMemModel",
    "AutoModelForCausalLM": "modeling_livemem.LiveMemForCausalLM"
  },
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "dtype": "bfloat16",
  "eos_token_id": 151645,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2560,
  "initializer_range": 0.02,
  "intermediate_size": 9728,
  "layer_types": [
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
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    "full_attention",
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    "full_attention",
    "full_attention",
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    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention"
  ],
  "max_position_embeddings": 262144,
  "max_window_layers": 36,
  "mem_conv_bias": false,
  "mem_conv_size": 4,
  "mem_expand_v": 1.0,
  "mem_head_dim": 128,
  "mem_layers": null,
  "mem_norm_eps": 1e-06,
  "mem_num_heads": 32,
  "mem_num_v_heads": 32,
  "mem_o_proj_zero_init": true,
  "memory_design": "X",
  "model_type": "livemem",
  "num_attention_heads": 32,
  "num_hidden_layers": 36,
  "num_key_value_heads": 8,
  "pad_token_id": 151643,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 5000000,
    "rope_type": "default"
  },
  "sliding_window": null,
  "tie_word_embeddings": true,
  "transformers_version": "5.12.1",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 151936
}