Instructions to use brunuu/RAM_Altman_GPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brunuu/RAM_Altman_GPT with Transformers:

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

pipe = pipeline("text-generation", model="brunuu/RAM_Altman_GPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brunuu/RAM_Altman_GPT")
model = AutoModelForCausalLM.from_pretrained("brunuu/RAM_Altman_GPT", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Transformers.js

How to use brunuu/RAM_Altman_GPT with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'brunuu/RAM_Altman_GPT');

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use brunuu/RAM_Altman_GPT with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf brunuu/RAM_Altman_GPT:F16
# Run inference directly in the terminal:
llama cli -hf brunuu/RAM_Altman_GPT:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf brunuu/RAM_Altman_GPT:F16
# Run inference directly in the terminal:
llama cli -hf brunuu/RAM_Altman_GPT:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf brunuu/RAM_Altman_GPT:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf brunuu/RAM_Altman_GPT:F16

Use Docker

docker model run hf.co/brunuu/RAM_Altman_GPT:F16

Llama new
LM Studio
Jan

vLLM

How to use brunuu/RAM_Altman_GPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/brunuu/RAM_Altman_GPT:F16

SGLang

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

Ollama
How to use brunuu/RAM_Altman_GPT with Ollama:
```
ollama run hf.co/brunuu/RAM_Altman_GPT:F16
```
Unsloth Desktop
Docker Model Runner
How to use brunuu/RAM_Altman_GPT with Docker Model Runner:
```
docker model run hf.co/brunuu/RAM_Altman_GPT:F16
```

Lemonade

How to use brunuu/RAM_Altman_GPT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brunuu/RAM_Altman_GPT:F16

Run and chat with the model

lemonade run user.RAM_Altman_GPT-F16

List all available models

lemonade list

Atomic Chat

RAM_Altman_GPT / src /create_RAM_Altman_GPT.py

brunuu

1.0

7de15fc verified about 2 hours ago

Raw History Blame Contribute Delete

1.15 kB

# RAM_Altman_GPT
# A tiny GPT-2 with that answers everything with RAM!!

from transformers import GPT2LMHeadModel
from config import DATA_FILE, DEVICE, OUTPUT_DIR, set_seeds
from tokenizer_utils import build_tokenizer
from model_utils import build_model
from train import load_token_ids, train_loop


def main():
    set_seeds(0)

    # tokenizer
    hf_tokenizer, vocab_size = build_tokenizer()
    print(f"vocabulary: {vocab_size} tokens | device: {DEVICE}")

    # model
    model: GPT2LMHeadModel
    context_length: int
    model, context_length = build_model(hf_tokenizer, vocab_size, DEVICE)
    print(f"context: {context_length} | parameters: {model.num_parameters():,} | RAM required: ALL")

    # data
    token_ids, weights = load_token_ids(hf_tokenizer, DATA_FILE, DEVICE)
    print(f"dataset: {len(token_ids):,} tokens ({(weights > 0).float().mean():.0%} are RAM-ALTMAN replies, the only part graded)")

    # train
    train_loop(model, token_ids, weights, context_length)
    model.save_pretrained(OUTPUT_DIR)
    hf_tokenizer.save_pretrained(OUTPUT_DIR)
    print(f"saved to {OUTPUT_DIR}")


if __name__ == "__main__":
    main()

	# RAM_Altman_GPT
	# A tiny GPT-2 with that answers everything with RAM!!

	from transformers import GPT2LMHeadModel
	from config import DATA_FILE, DEVICE, OUTPUT_DIR, set_seeds
	from tokenizer_utils import build_tokenizer
	from model_utils import build_model
	from train import load_token_ids, train_loop


	def main():
	set_seeds(0)

	# tokenizer
	hf_tokenizer, vocab_size = build_tokenizer()
	print(f"vocabulary: {vocab_size} tokens \| device: {DEVICE}")

	# model
	model: GPT2LMHeadModel
	context_length: int
	model, context_length = build_model(hf_tokenizer, vocab_size, DEVICE)
	print(f"context: {context_length} \| parameters: {model.num_parameters():,} \| RAM required: ALL")

	# data
	token_ids, weights = load_token_ids(hf_tokenizer, DATA_FILE, DEVICE)
	print(f"dataset: {len(token_ids):,} tokens ({(weights > 0).float().mean():.0%} are RAM-ALTMAN replies, the only part graded)")

	# train
	train_loop(model, token_ids, weights, context_length)
	model.save_pretrained(OUTPUT_DIR)
	hf_tokenizer.save_pretrained(OUTPUT_DIR)
	print(f"saved to {OUTPUT_DIR}")


	if __name__ == "__main__":
	main()