Instructions to use botverse/Titans-MAC-Llama-3-8b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use botverse/Titans-MAC-Llama-3-8b-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="botverse/Titans-MAC-Llama-3-8b-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("botverse/Titans-MAC-Llama-3-8b-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("botverse/Titans-MAC-Llama-3-8b-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use botverse/Titans-MAC-Llama-3-8b-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "botverse/Titans-MAC-Llama-3-8b-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": "botverse/Titans-MAC-Llama-3-8b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/botverse/Titans-MAC-Llama-3-8b-Instruct

SGLang

How to use botverse/Titans-MAC-Llama-3-8b-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 "botverse/Titans-MAC-Llama-3-8b-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": "botverse/Titans-MAC-Llama-3-8b-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 "botverse/Titans-MAC-Llama-3-8b-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": "botverse/Titans-MAC-Llama-3-8b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use botverse/Titans-MAC-Llama-3-8b-Instruct with Docker Model Runner:
```
docker model run hf.co/botverse/Titans-MAC-Llama-3-8b-Instruct
```

Memory-Augmented Llama Model (Llama-3-8B-Instruct)

This repository contains the base weights for Llama-3-8B-Instruct packaged with custom code for the InferenceMemoryWrapper.

This allows loading the model with memory capabilities using trust_remote_code=True.

Model Details

Base Model: Llama-3-8B-Instruct
Wrapper: InferenceMemoryWrapper
Memory Size: 4096
Memory Dims: 4096
Memory Storage (approx): 64.0 MB (FP16, buffer + state) if buffer/state exist

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch # Added import for example

model_id = "your-username/your-repo-name" # Replace with your repo ID

# Load the model and tokenizer, allowing custom code execution
# Requires sufficient VRAM for the Llama 8B model + memory buffer
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16, # Recommended for memory
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Example prompt
prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate using the custom method
# Note: The memory buffer is initially randomly initialized unless loaded separately.
# It will be updated during generation if update_rule is 'ema' or 'surprise'.
outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    use_memory=True,
    update_rule='ema' # or 'surprise' or 'none'
)

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

# To save user-specific memory state (after generation/updates):
# user_memory_state = model.memory_buffer.data.clone()
# user_surprise_state = model.surprise_state.clone()
# torch.save({'memory_buffer': user_memory_state, 'surprise_state': user_surprise_state}, 'user_memory.pt')

# To load user-specific memory state:
# loaded_state = torch.load('user_memory.pt')
# model.memory_buffer.data.copy_(loaded_state['memory_buffer'])
# model.surprise_state.copy_(loaded_state['surprise_state'])

Important: The memory_buffer and surprise_state in this packaged model are initialized randomly according to the InferenceMemoryWrapper code. They do not contain any pre-trained memory state unless you load it separately after initializing the model (see example above). You need to manage loading/saving the memory state per user externally.

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Safetensors

Model size

8B params

Tensor type

BF16