Instructions to use CodeSoft/sorbet-mini-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeSoft/sorbet-mini-experimental with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CodeSoft/sorbet-mini-experimental")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeSoft/sorbet-mini-experimental")
model = AutoModelForCausalLM.from_pretrained("CodeSoft/sorbet-mini-experimental", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CodeSoft/sorbet-mini-experimental with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeSoft/sorbet-mini-experimental:F16
# Run inference directly in the terminal:
llama cli -hf CodeSoft/sorbet-mini-experimental:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeSoft/sorbet-mini-experimental:F16
# Run inference directly in the terminal:
llama cli -hf CodeSoft/sorbet-mini-experimental:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf CodeSoft/sorbet-mini-experimental:F16
# Run inference directly in the terminal:
./llama-cli -hf CodeSoft/sorbet-mini-experimental:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf CodeSoft/sorbet-mini-experimental:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodeSoft/sorbet-mini-experimental:F16

Use Docker

docker model run hf.co/CodeSoft/sorbet-mini-experimental:F16

LM Studio
Jan

vLLM

How to use CodeSoft/sorbet-mini-experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeSoft/sorbet-mini-experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/sorbet-mini-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeSoft/sorbet-mini-experimental:F16

SGLang

How to use CodeSoft/sorbet-mini-experimental with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CodeSoft/sorbet-mini-experimental" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/sorbet-mini-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CodeSoft/sorbet-mini-experimental" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/sorbet-mini-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use CodeSoft/sorbet-mini-experimental with Ollama:
```
ollama run hf.co/CodeSoft/sorbet-mini-experimental:F16
```
Unsloth Desktop
Docker Model Runner
How to use CodeSoft/sorbet-mini-experimental with Docker Model Runner:
```
docker model run hf.co/CodeSoft/sorbet-mini-experimental:F16
```

Lemonade

How to use CodeSoft/sorbet-mini-experimental with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CodeSoft/sorbet-mini-experimental:F16

Run and chat with the model

lemonade run user.sorbet-mini-experimental-F16

List all available models

lemonade list

Atomic Chat

sorbet-mini-experimental / README.md

CodeSoft

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	---
	license: apache-2.0
	datasets:
	- roneneldan/TinyStories
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- sorbet
	- sorbet-mini
	---

	# 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?utm_source=huggingface&utm_medium=embedded_model_card&utm_campaign=CodeSoft_sorbet-mini-experimental_card" target="_blank" rel="noopener">
	<img
	src="https://hfviewer.com/api/card.svg?source=CodeSoft%2Fsorbet-mini-experimental&granularity=0"
	alt="Architecture graph for CodeSoft/sorbet-mini-experimental. Open in hfviewer"
	width="100%"
	/>
	</a>

	\| knob \| value \|
	\|---\|---\|
	\| tokens \| 149,999,872 (~37× Chinchilla) \|
	\| steps \| 1144 @ 512 seqs/step (seq 256) \|
	\| data \| TinyStories \|
	\| precision \| bf16, 8-bit AdamW \|
	\| optimizer \| AdamW lr 3e-4 → 1e-5 cosine, wd 0.1 (no decay on emb/norm), grad clip 1.0 \|
	\| hardware \| RTX 5060 Ti 16GB \|

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

	## Run it

	```bash
	# 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)

	```python
	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))
	```