Instructions to use LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch with Transformers:

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

pipe = pipeline("text-generation", model="LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch")
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 LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch

SGLang

How to use LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch 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 "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch" \
    --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": "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch",
		"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 "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch" \
        --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": "LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch
```

LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch

Summary

Base model: google/gemma-4-26B-A4B
Source dataset/cache: /home/work/.data/gemma4_native_sft/datasets/google__gemma-4-26B-A4B__liquid_raw_json_masked_8192
Training format: Gemma 4 native chat template
Labels: assistant JSON command response only
Prompt/history labels are masked with -100
Previous assistant thinking blocks are stripped from history

TB2-lite

Result: pending

Notes

Source checkpoint: /home/work/.data/gemma4_native_sft/models/google__gemma-4-26B-A4B__terminal_sft_native_liquid_2epoch/checkpoint-510
Checkpoint step: 510
Trainer epoch: 1.0000
TB2-lite score: pending GPU evaluation
Upload policy: checkpoint uploaded immediately after save; score card updates after evaluation.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch", torch_dtype="auto")

Downloads last month: 14

Safetensors

Model size

6B params

Tensor type

BF16

Model tree for LLM-OS-Models/gemma-4-26B-A4B-Terminal-SFT-Native-Liquid-1Epoch

Base model

google/gemma-4-26B-A4B

Finetuned

(22)

this model