Instructions to use scasella91/talkie-1930-13b-it-MLX-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use scasella91/talkie-1930-13b-it-MLX-q4 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("scasella91/talkie-1930-13b-it-MLX-q4") 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 scasella91/talkie-1930-13b-it-MLX-q4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "scasella91/talkie-1930-13b-it-MLX-q4"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "scasella91/talkie-1930-13b-it-MLX-q4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "scasella91/talkie-1930-13b-it-MLX-q4", "messages": [ {"role": "user", "content": "Hello"} ] }'
Talkie 1930 13B IT MLX q4
Unofficial community MLX q4 release of
lewtun/talkie-1930-13b-it-hf,
prepared for local Apple Silicon inference with
Talkie Cabinet.
This is not an official Talkie release. It is a Mac-oriented companion to the
browser/WebGPU ONNX release:
scasella91/talkie-1930-13b-it-ONNX.
Credit To Talkie
This repo packages talkie-1930-13b-it, a 13B-parameter instruction-tuned
language model from the talkie family
developed by Alec Radford, Nick Levine, and David Duvenaud. Talkie was
pretrained on 260B tokens of pre-1931 English-language text and
instruction-tuned with a dataset extracted from vintage reference works,
including etiquette manuals, encyclopedias, letter-writing guides, and poetry
collections. The instruction model also used reinforcement learning via online DPO
with an LLM-as-a-judge to improve instruction following.
Read more in the Talkie report.
Files
The repo contains the MLX model shards plus the tokenizer/config/chat-template files needed by Talkie Cabinet:
model-00001-of-00002.safetensorsmodel-00002-of-00002.safetensorsmodel.safetensors.index.jsonconfig.jsongeneration_config.jsontokenizer.jsontokenizer_config.jsonchat_template.jinjatalkie_mlx.py
Validation Notes
The published q4-safe candidate keeps the language-model head and attention value projections in BF16. It uses more memory than the smaller q4 experiment, but avoids the short-prompt collapse observed in that smaller artifact.
Observed on a MacBook Pro with M4 Pro and 24 GB unified memory:
- On-disk model directory: about 8.8 GB.
- App-reported peak memory: about 9.7 GB.
- Typical short-turn decode after load: roughly 20-25 tok/s.
- Local load after setup: about 3 seconds.
Use With Talkie Cabinet
git clone https://github.com/scasella/talkie-mlx-macos.git
cd talkie-mlx-macos
./scripts/download_model.sh
./scripts/run_app.sh
The setup script downloads this repo to:
~/Library/Application Support/Talkie Cabinet/Models/talkie-1930-13b-it-MLX-q4
Attribution
- Original Talkie researchers: Alec Radford, Nick Levine, and David Duvenaud
- Talkie report:
talkie-lm.com - Source model:
lewtun/talkie-1930-13b-it-hf - Original project:
talkie-lm/talkie - Mac app:
scasella/talkie-mlx-macos - Browser/WebGPU release:
scasella/talkie-quant-webgpu
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Model tree for scasella91/talkie-1930-13b-it-MLX-q4
Base model
talkie-lm/talkie-1930-13b-base