Text Generation
MLX
Safetensors
English
llama
alignment-handbook
trl
sft
conversational
4-bit precision
Instructions to use golangboy/Aicount with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use golangboy/Aicount with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("golangboy/Aicount") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use golangboy/Aicount with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "golangboy/Aicount"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "golangboy/Aicount" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "golangboy/Aicount", "messages": [ {"role": "user", "content": "Hello"} ] }'
| license: apache-2.0 | |
| base_model: HuggingFaceTB/SmolLM-135M-Instruct | |
| tags: | |
| - alignment-handbook | |
| - trl | |
| - sft | |
| - mlx | |
| datasets: | |
| - Magpie-Align/Magpie-Pro-300K-Filtered | |
| - bigcode/self-oss-instruct-sc2-exec-filter-50k | |
| - teknium/OpenHermes-2.5 | |
| - HuggingFaceTB/everyday-conversations-llama3.1-2k | |
| library_name: mlx | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |