Instructions to use N8Programs/talkie-box with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use N8Programs/talkie-box 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("N8Programs/talkie-box") 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
- LM Studio
- MLX LM
How to use N8Programs/talkie-box with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "N8Programs/talkie-box"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "N8Programs/talkie-box" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N8Programs/talkie-box", "messages": [ {"role": "user", "content": "Hello"} ] }'
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@@ -12,10 +12,12 @@ A lightly post-trained version of [talkie-lm/talkie-1930-13b-base](https://huggi
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Recommended sampling settings are `temp=0.5, min_p=0.05, top_k=40, repetition_penalty=1.2, repetition_context_size=128`. Like the base model, it has a max context size of 2048. It additionally retians the (limited) few shot learning ability of the base model - going from 7.73% GSM8K at 1-shot to 11.30% at 2-shot to 12.36% at 4-shot.
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## Safety Disclaimer
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This model is ancient and safety training is minimal. It may generate objectionable content, hateful content, or flat-up wrong content. Thus it is strictly for experimental purposes and I strongly recommend against its use in any user-facing application.
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## Chat template
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This model uses a standard `user` / `assistant` chat API surface, but renders
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Recommended sampling settings are `temp=0.5, min_p=0.05, top_k=40, repetition_penalty=1.2, repetition_context_size=128`. Like the base model, it has a max context size of 2048. It additionally retians the (limited) few shot learning ability of the base model - going from 7.73% GSM8K at 1-shot to 11.30% at 2-shot to 12.36% at 4-shot.
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The seed prompts used for initial persona elicitationcan be found [here](https://gist.github.com/N8python/00fda0195d4923908f6b1f0bd7337208) - each 'seed' was then continued by base talkie into a multi-turn conversation, the best of which formed the initial SFT round.
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## Safety Disclaimer
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This model is ancient and safety training is minimal. It may generate objectionable content, hateful content, or flat-up wrong content. Thus it is strictly for experimental purposes and I strongly recommend against its use in any user-facing application.
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## Chat template
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This model uses a standard `user` / `assistant` chat API surface, but renders
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