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/convert_gguf.py from brunuu/RAM_Altman_GPT: direct link, hf CLI and curl.
- Browser
- Download file 504 Bytes
-
https://huggingface.co/brunuu/RAM_Altman_GPT/resolve/main/src/convert_gguf.py
- Command line
-
hf download hf://brunuu/RAM_Altman_GPT/src/convert_gguf.py
-
curl -L -o convert_gguf.py https://huggingface.co/brunuu/RAM_Altman_GPT/resolve/main/src/convert_gguf.py
504 Bytes
| import sys | |
| from pathlib import Path | |
| from config import OUTPUT_DIR | |
| from llama_setup import TextModel, convert_hf_to_gguf | |
| MODEL = Path(OUTPUT_DIR) | |
| OUT = MODEL / f"{MODEL.name}-f16.gguf" | |
| if not (MODEL / "model.safetensors").exists(): | |
| sys.exit("NO MODEL FOUND!") | |
| TextModel.get_vocab_base_pre = lambda self, tokenizer: "gpt-2" | |
| sys.argv = ["convert_hf_to_gguf.py", str(MODEL), "--outfile", str(OUT), "--outtype", "f16"] | |
| convert_hf_to_gguf.main() | |
| print(f"done: {OUT} ({OUT.stat().st_size // 1024} KB)") | |