CodeSoft/sorbet-mini-experimental

Sorbet Mini Experimental

This model isn't meant to be "good", yet. The whole point of Sorbet Mini Experimental is to have a base fast enough that you can iterate on without burning hours per experiment. It exists to make the bigger experiments cheaper.

What makes this so important for Sorbet

Sorbet Mini Experimental was trained on 150M tokens, in 12 minutes, with one RTX 5060 Ti.

What matters is that a full pretrain run in twelve minutes means every change on the Sorbet line can be tested quickly without burning hours on a larger model.

## What's next for Sorbet Mini Since Sorbet Mini is so cheap to train, it's a no brainer to keep training it. TinyStories was used to target basic language coherence as a starting point. Eventually, the full release, Sorbet Mini, will release and hopefully perform closer to other similarly sized models.

What it is

• —Arch: Qwen2ForCausalLM (native in transformers and llama.cpp)
• —Shape: h192 × 8 layers, heads 6 (dim 32), GQA kv=1, inter 576, tied embeddings
• —Vocab: 8192 (same tokenizer as the sorbet-25m family)
• —Params: 4,920,512 total | bf16 ≈ 9.9 MB | Q8_0 ≈ 5.2 MB
• —Context: 256 train / up to 512 inference

Training recipe

Architecture graph

<a href="https://hfviewer.com/CodeSoft/sorbet-mini-experimental?utmsource=huggingface&amp;utmmedium=embeddedmodelcard&amp;utmcampaign=CodeSoftsorbet-mini-experimentalcard" target="blank" rel="noopener"> <img src="https://hfviewer.com/api/card.svg?source=CodeSoft%2Fsorbet-mini-experimental&amp;granularity=0" alt="Architecture graph for CodeSoft/sorbet-mini-experimental. Open in hfviewer" width="100%" /> </a>

Result: train loss 8.13 → 2.81, val perplexity 3595 → 19.47.

Run it

# very close to f16 (recommended)
llama-completion -m sorbet-mini-experimental-q8_0.gguf \
  -p "Once upon a time," -n 128 --temp 0.8 --top-p 0.95

# reference full-precision build
llama-cli -m sorbet-mini-experimental-f16.gguf -p "Hello, " -n 32

Run the safetensors (transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("CodeSoft/sorbet-mini-experimental")
model = AutoModelForCausalLM.from_pretrained("CodeSoft/sorbet-mini-experimental", dtype=torch.bfloat16)
# model.to("cuda")  # if you have a GPU

prompt = "Once upon a time,"
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=128, do_sample=True,
                     temperature=0.8, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))