Instructions to use soob3123/amoral-gemma3-27B-v2-qat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use soob3123/amoral-gemma3-27B-v2-qat with Transformers:

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

pipe = pipeline("text-generation", model="soob3123/amoral-gemma3-27B-v2-qat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("soob3123/amoral-gemma3-27B-v2-qat")
model = AutoModelForCausalLM.from_pretrained("soob3123/amoral-gemma3-27B-v2-qat", device_map="auto")
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 Settings

vLLM

How to use soob3123/amoral-gemma3-27B-v2-qat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "soob3123/amoral-gemma3-27B-v2-qat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soob3123/amoral-gemma3-27B-v2-qat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/soob3123/amoral-gemma3-27B-v2-qat

SGLang

How to use soob3123/amoral-gemma3-27B-v2-qat 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 "soob3123/amoral-gemma3-27B-v2-qat" \
    --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": "soob3123/amoral-gemma3-27B-v2-qat",
		"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 "soob3123/amoral-gemma3-27B-v2-qat" \
        --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": "soob3123/amoral-gemma3-27B-v2-qat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use soob3123/amoral-gemma3-27B-v2-qat with Docker Model Runner:
```
docker model run hf.co/soob3123/amoral-gemma3-27B-v2-qat
```

soob3123 commited on Apr 20, 2025

Commit

729222a

verified ·

1 Parent(s): d867707

Unsloth Model Card

Browse files

Files changed (1) hide show

README.md +9 -20

README.md CHANGED Viewed

@@ -1,32 +1,21 @@
 ---
-base_model: google/gemma-3-12b-it-qat-q4_0-unquantized
 tags:
 - text-generation-inference
 - transformers
 - gemma3
-- analytical-tasks
-- bias-neutralization
-- uncensored
 language:
 - en
-license: apache-2.0
-pipeline_tag: text-generation
-datasets:
-- TheDrummer/AmoralQA-v2
 ---
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f93f9477b722f1866398c2/eNraUCUocrOhowWdIdtod.png)
-> "Neutrality is not indifference. It is engagement with equal intensity."
-> ― J. Robert Oppenheimer *[Lecture on Scientific Ethics, 1957]*
-## QAT version of Amoral-Gemma-3
-**Core Function:**
-- Produces analytically neutral responses to sensitive queries
-- Maintains factual integrity on controversial subjects
-- Avoids value-judgment phrasing patterns
-**Response Characteristics:**
-- No inherent moral framing ("evil slop" reduction)
-- Emotionally neutral tone enforcement
-- Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)

 ---
+base_model: google/gemma-3-27b-it-qat-q4_0-unquantized
 tags:
 - text-generation-inference
 - transformers
+- unsloth
 - gemma3
+license: apache-2.0
 language:
 - en
 ---
+# Uploaded finetuned  model
+- **Developed by:** soob3123
+- **License:** apache-2.0
+- **Finetuned from model :** google/gemma-3-27b-it-qat-q4_0-unquantized
+This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)