Instructions to use KeeganCarey/gemma-3-1b-it-amr_thinking-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KeeganCarey/gemma-3-1b-it-amr_thinking-2 with Transformers:

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

pipe = pipeline("text-generation", model="KeeganCarey/gemma-3-1b-it-amr_thinking-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("KeeganCarey/gemma-3-1b-it-amr_thinking-2")
model = AutoModelForCausalLM.from_pretrained("KeeganCarey/gemma-3-1b-it-amr_thinking-2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KeeganCarey/gemma-3-1b-it-amr_thinking-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KeeganCarey/gemma-3-1b-it-amr_thinking-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KeeganCarey/gemma-3-1b-it-amr_thinking-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KeeganCarey/gemma-3-1b-it-amr_thinking-2

SGLang

How to use KeeganCarey/gemma-3-1b-it-amr_thinking-2 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 "KeeganCarey/gemma-3-1b-it-amr_thinking-2" \
    --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": "KeeganCarey/gemma-3-1b-it-amr_thinking-2",
		"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 "KeeganCarey/gemma-3-1b-it-amr_thinking-2" \
        --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": "KeeganCarey/gemma-3-1b-it-amr_thinking-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KeeganCarey/gemma-3-1b-it-amr_thinking-2 with Docker Model Runner:
```
docker model run hf.co/KeeganCarey/gemma-3-1b-it-amr_thinking-2
```

gemma-3-1b-it-amr_thinking-2 / README.md

Keegan Carey

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metadata

license: gemma
tags:
  - gemma
  - gemma3
  - tunix
  - grpo
  - reasoning
  - thinking
library_name: transformers
language:
  - en
base_model:
  - chimbiwide/gemma-3-1b-it-thinking-32k-sft-base

GemmaThink-32k (GRPO Trained)

This model was trained using GRPO (Group Relative Policy Optimization) to generate structured reasoning traces.

Training Details

Base Model: chimbiwide/gemma-3-1b-it-thinking-32k-sft-base
Training Method: SFT + GRPO
LoRA Rank: 32
LoRA Alpha: 64.0
Framework: Tunix (JAX)
Hardware: v6e-1 TPU in Colab

Output Format

<reasoning>step-by-step thinking process</reasoning>
<answer>final answer</answer>

Quicklinks:

SFT Base Model
SFT Base Model Q8 GGUF
GRPO Full Model <-- You're here
Q8-GGUF
Article