Instructions to use GetSoloTech/FoodStack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GetSoloTech/FoodStack with Transformers:

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

pipe = pipeline("text-generation", model="GetSoloTech/FoodStack")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GetSoloTech/FoodStack")
model = AutoModelForCausalLM.from_pretrained("GetSoloTech/FoodStack")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GetSoloTech/FoodStack with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GetSoloTech/FoodStack"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GetSoloTech/FoodStack",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GetSoloTech/FoodStack

SGLang

How to use GetSoloTech/FoodStack 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 "GetSoloTech/FoodStack" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GetSoloTech/FoodStack",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "GetSoloTech/FoodStack" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GetSoloTech/FoodStack",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use GetSoloTech/FoodStack with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GetSoloTech/FoodStack to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GetSoloTech/FoodStack to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GetSoloTech/FoodStack to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="GetSoloTech/FoodStack",
    max_seq_length=2048,
)

Docker Model Runner
How to use GetSoloTech/FoodStack with Docker Model Runner:
```
docker model run hf.co/GetSoloTech/FoodStack
```

zeeshaan-ai commited on Mar 19

Commit

bb15e2c

verified ·

1 Parent(s): 32debd9

Add Solo model card

Browse files

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README.md +39 -0

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	@@ -0,0 +1,39 @@

+---
+library_name: transformers
+base_model: google/gemma-3-270m-it
+tags: [solo, fine-tuned, lora, unsloth]
+datasets: [GetSoloTech/Code-Reasoning]
+pipeline_tag: text-generation
+---
+<a href="https://hub.getsolo.tech"><img src="https://raw.githubusercontent.com/GetSoloTech/solo-cli/main/media/solo-banner.png" alt="Solo" width="200"></a>
+## Model Details
+| | |
+|---|---|
+| **Base Model** | [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it) |
+| **Method** | LoRA (PEFT) |
+| **Parameters** | 0.27B |
+## Training Hyperparameters
+| | |
+|---|---|
+| **Epochs** | 1 |
+| **Max Steps** | 100 |
+| **Batch Size** | 4 |
+| **Gradient Accumulation** | 4 |
+| **Learning Rate** | 0.0002 |
+| **LoRA r** | 4 |
+| **LoRA Alpha** | 4 |
+| **Max Sequence Length** | 2048 |
+| **Training Duration** | 41m 11s |
+## Dataset
+[GetSoloTech/Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
+---
+<sub>Trained with <a href="https://hub.getsolo.tech">Solo</a></sub>