Text Generation
Transformers
ONNX
Safetensors
GGUF
Transformers.js
English
gpt2
humor
parody
tiny
chat
lm-studio
ollama
llama.cpp
gpt
meme
funny
joke
satire
ram
ram-shortage
tiny-model
small-model
micro-model
toy-model
trained-from-scratch
custom-tokenizer
synthetic-data
conversational
chatbot
cpu
low-resource
text-generation-inference
Instructions to use brunuu/RAM_Altman_GPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download src/config.py from brunuu/RAM_Altman_GPT: direct link, hf CLI and curl.
- Browser
- Download file 857 Bytes
-
https://huggingface.co/brunuu/RAM_Altman_GPT/resolve/main/src/config.py
- Command line
-
hf download hf://brunuu/RAM_Altman_GPT/src/config.py
-
curl -L -o config.py https://huggingface.co/brunuu/RAM_Altman_GPT/resolve/main/src/config.py
857 Bytes
| from pathlib import Path | |
| import torch | |
| import random | |
| PROJECT_ROOT = Path(__file__).resolve().parent.parent | |
| DATA_FILE = str(PROJECT_ROOT / "data" / "ram_altman.txt") | |
| OUTPUT_DIR = str(PROJECT_ROOT / "RAM_Altman_GPT") | |
| END_TOKEN = "<|i_have_enough_ram|>" | |
| TARGET_PARAMS = 100_000 | |
| EMBEDDING_SIZE = 40 | |
| NUM_LAYERS = 3 | |
| NUM_HEADS = 4 | |
| VOCAB_SIZE = 640 | |
| STEPS = 30000 | |
| BATCH_SIZE = 32 | |
| LEARNING_RATE = 4e-3 | |
| CHAT_TEMPLATE = ( | |
| "{% for message in messages %}" | |
| "{% if message['role'] == 'user' %}User: {{ message['content'] }}\n" | |
| "{% elif message['role'] == 'assistant' %}RAM-ALTMAN: {{ message['content'] }}<|i_have_enough_ram|>\n" | |
| "{% endif %}" | |
| "{% endfor %}" | |
| "{% if add_generation_prompt %}RAM-ALTMAN:{% endif %}" | |
| ) | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| def set_seeds(seed=0): | |
| torch.manual_seed(seed) | |
| random.seed(seed) | |