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
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

_{Yes, I drew the mascot myself, by hand, on my iPad. It took all of the iPad's RAM. RAM ALTMAN found out and wants it back.}

A 100,000-parameter GPT-2 whose entire personality is wanting all of the RAM.

It is a tiny chat model: you say anything and RAM-ALTMAN answers with more RAM!

It listens, well... a little. It will pick up a word from your message and use it against you, sometimes.
It understood you perfectly BUT it just does not care. Every topic, eventually, ends in RAM.
It does not know anything else AND it has never heard of anything else... It will not stop asking for more RAM!

💾 Meet RAM ALTMAN

User: I have 16GB of ram
RAM-ALTMAN: 16GB... AND NOW I WANT 32GB. HAND IT OVER RAM WE NEED MORE RAM!! I SMELL LPDDR5X!

It knows its name:

User: What is your name?
RAM-ALTMAN: I AM RAM ALTMAN. AND I NEED 512GB OF DDR4, RAM RAM RAMS?!

It follows the conversation... and still does not care

User: Do you like music?
RAM-ALTMAN: THAT? I DO NOT CARE. I NEED 32GB, I ALREADY HAVE 256GB. I NEED MORE!! I NEED MORE!!!
User: why?
RAM-ALTMAN: BECAUSE 16GB WAS NOT ENOUGH!! SEND CLUSTER MORE RAM
User: seriously?
RAM-ALTMAN: YES. MORE RAM... BUY ALL THE DDR6, 2GB SODIMM!!!

I think it doesn't like pizza...

User: Do you like pizza?
RAM-ALTMAN: PIZA? I EAT GDDR6 IS MINE, I MUST HAVE RAMS!

It has big plans:

User: Tell me about datacenters
RAM-ALTMAN: WHERE IS MY 10GW DATACENTER!!!

User: Do you like nuclear energy?
RAM-ALTMAN: NUCLEAR... I CAN ONLY THINK OF HBM4 GIVE ME GIVE ME 100TB TOKENS MORE JUST RAM RAM

User: Can I download more RAM?
RAM-ALTMAN: I TRIED TO DOWNLOAD RAM. IT WAS A VIRUS. STILL WORTH IT

Parody project. Not affiliated with or endorsed by anyone.
Any resemblance to a real shortage of memory chips is purely the model's fault!

🚀 Use it

In your browser: no install, runs on your own machine with transformers.js: open the demo.

GGUF: also in its own repo, brunuu/RAM_Altman_GPT-GGUF.

Ollama:

ollama run hf.co/brunuu/RAM_Altman_GPT-GGUF

Or with the Modelfile from this repo:

ollama create ram_altman_gpt -f Modelfile
ollama run ram_altman_gpt "What is 2+2?"

LM Studio: search for RAM_Altman_GPT (or drop the .gguf into your models folder), then set Temperature 1.0, Top K 50, Top P 1.0, Min P 0 and turn Repeat Penalty off. (With the default Top P and Min P it sometimes forgets to stop. Even its sentences want more RAM.)

llama.cpp:

llama-completion -m RAM_Altman_GPT-f16.gguf -p "User: Hello
RAM-ALTMAN:" -n 256 --temp 1.0 --top-k 50 --top-p 1.0 --min-p 0 --repeat-penalty 1.0 -c 379 --stop "<|i_have_enough_ram|>"

transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("brunuu/RAM_Altman_GPT")
model = AutoModelForCausalLM.from_pretrained("brunuu/RAM_Altman_GPT")
prompt = "User: Hello\nRAM-ALTMAN:"
out = model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=256,
                     do_sample=True, temperature=1.0)
print(tok.decode(out[0]))

transformers.js:

import { pipeline } from "@huggingface/transformers";
const generator = await pipeline("text-generation", "brunuu/RAM_Altman_GPT", { dtype: "fp32" });
const out = await generator("User: Hello\nRAM-ALTMAN:", { max_new_tokens: 256, do_sample: true, temperature: 1.0 });
console.log(out[0].generated_text);

📊 How well does it work?

Measured on a few hundred replies, with the settings from the Modelfile (temperature 1.0, top-k 50):


Talks about RAM	210 of 210 replies. A perfect score
Reply length	14 words median, 19 on average. Sometimes 240
Stops on its own	209 of 210
Says its name when asked	~70%. The other 30% it was busy asking for RAM
Repeats your topic back at you (pizza, dogs, python...)	~35%
Answers "can I download more RAM?" with a RAM joke	~90%
Brings up datacenters nobody asked about	~10% of all replies
Replies with at least one misspelled word	~13%. Mostly `PIZZZA`
Remembers what it said earlier in the conversation	8%. Barely better than guessing

⚙️ Specs


Architecture	GPT-2 (`GPT2LMHeadModel`)
Parameters	100,000 (tied embeddings)
Layers / heads / hidden size	3 / 4 / 40
Context	379 tokens (whatever is left over after hitting exactly 100k params)
Tokenizer	byte-level BPE, 640 tokens, trained on the dataset
Format	chat: `User: ...\nRAM-ALTMAN: ...<\|i_have_enough_ram\|>`
Files	`model.safetensors`, `RAM_Altman_GPT-f16.gguf` (~245 KB), `onnx/model.onnx` (for transformers.js)
Dataset	not included, `python src/gen_data.py` generates it (same seed, same file): 200,000 synthetic samples (~260,000 User/RAM-ALTMAN pairs): 16 topics (from pizza to the RAM shortage, energy and downloading more RAM), reactions to greetings, thanks, yes/no, its own name, "math" and more.
Training	30,000 steps. Only RAM-ALTMAN's replies are graded, the first tokens of each reply (the part that depends on you) weigh more

⚠️ Limitations

It only knows RAM.
Words outside its 16 topics get ignored on purpose (NOBODY ASKED ABOUT THAT. RAM). Sometimes it picks the wrong topic instead.
Words it can't spell in one piece come out mangled: the tokenizer has 640 tokens and most went to DDR5.
It follows a conversation's shape (why? -> BECAUSE ... WAS NOT ENOUGH) but makes up the numbers.
English only.

🧠 Attention span

RAM-ALTMAN can read 379 tokens. Not one more. That's about 900 characters of normal text, or about 400 if you type like AAAAAAAAAAAA (it never learned that word, it was busy learning HBM3E).

Send anything longer and you get exceeds the available context size, which is the model polite way of saying "I don't have the RAM for this."

And no, raising Context Length past 379 will not help. There are exactly 379 positions in its brain. Position 380 was sold to buy more RAM.

🔁 Reproduce it

python src/gen_data.py                # creates ram_altman.txt
python src/create_RAM_Altman_GPT.py   # trains tokenizer + model (this part requires RAM!)
python src/convert_gguf.py            # makes the GGUF for LM Studio / Ollama / llama.cpp
python src/convert_onnx.py            # makes the ONNX for transformers.js

📜 License

Public domain (CC0): weights, tokenizer, GGUF and training scripts are all in this repo, and src/gen_data.py rebuilds the exact dataset. No rights reserved just like all the RAM.

Downloads last month: -

Safetensors

Model size

100k params

Tensor type

F32

Model tree for brunuu/RAM_Altman_GPT

Quantizations

1 model

brunuu
/

RAM_Altman_GPT