Explaining how TLET AI public & internal model names work:
Public Model Name: {basemodel}-{parameters}-{type}-{series}-{hyperspecialization (when USM type)}-{release type number}{versioning number (digits are 3x what release type number digits are)}
Release Type: 0, Proof of Concept/Early Works stage
Release Version: 003, this is the 3rd model in the Tau series of models (includes fine-tunes that come from other base models.)
Internal/Private Model Name: {series}{release type number}-{hyperspecialization}-{versioning number (digits are 3x what release type number digits are)}-{parameters}-{ir IF model is Inference Ready (Ollama)}
So: Tau0-Py-003-4B-ir means:
Series: Tau (Proof of Concept model series)
Release Type: 0, Proof of Concept/Early Works stage
Hyperspecialization (is USM): Python
Release Version: 003, this is the 3rd model in the Tau series of models (includes fine-tunes that come from other base models.)
ollama run hf.co/tletai/phi-4-mini-instruct-4b-usm-tau-py-0003:Q5_K_M
Aliasing
While you are running it, you can run the following command to save it with it's much simpler internal name for ease of use via Ollama. (This command should be ran AFTER running ollama run, meaning, when you are already chatting with the model in the CLI.)
>>> /save tau0-py-003-4B-ir:Q5_K_M
This will allow you to run it using this command instead:
ollama run tau0-py-003-4B-ir:Q5_K_M
You can remove parts you don't want, such as "-4B-ir" or ":Q5_K_M" (which isn't really needed if you're just planning on downloading a single quantization anyways) from the /save command as you wish.
Deleting Aliases
This command will remove the alias but keep the model:
ollama rm tau0-py-003-4B-ir:Q5_K_M
Complete Deletion
To fully remove the model, remove all aliases and also remove the original pull:
Done using QLoRA, Paged AdamW 32-bit with an 8K Context Length on an NVidia RTX 3060 Ti (8GB VRAM) for 6h47m at about ~112W power draw for most of the time, with occassional hike-ups to at-most 217W (of therotical possible 225W.)
Tokens during training: 4699303.
Epochs completed: 1.33 (67% of 2 target epochs, runtime got too long for proof of concept, so it was cancelled. Steps for more precise, in our config: 3099/4654.)
Done using Unsloth Studio, which largely increased training efficency and speed.
If you need specifics for research purposes, possible collaboration, fine-tuning a model yourself or are just curious, feel free to reach out. We do not have specific, timed power usage data anymore. It was discarded immediately after it was used, do not ask for it.
