Instructions to use alphaedge-ai/gemma-3-1b-it-msa-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphaedge-ai/gemma-3-1b-it-msa-16384 with Transformers:

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

pipe = pipeline("text-generation", model="alphaedge-ai/gemma-3-1b-it-msa-16384")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/gemma-3-1b-it-msa-16384")
model = AutoModelForCausalLM.from_pretrained("alphaedge-ai/gemma-3-1b-it-msa-16384")
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 alphaedge-ai/gemma-3-1b-it-msa-16384 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alphaedge-ai/gemma-3-1b-it-msa-16384"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alphaedge-ai/gemma-3-1b-it-msa-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphaedge-ai/gemma-3-1b-it-msa-16384

SGLang

How to use alphaedge-ai/gemma-3-1b-it-msa-16384 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 "alphaedge-ai/gemma-3-1b-it-msa-16384" \
    --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": "alphaedge-ai/gemma-3-1b-it-msa-16384",
		"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 "alphaedge-ai/gemma-3-1b-it-msa-16384" \
        --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": "alphaedge-ai/gemma-3-1b-it-msa-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphaedge-ai/gemma-3-1b-it-msa-16384 with Docker Model Runner:
```
docker model run hf.co/alphaedge-ai/gemma-3-1b-it-msa-16384
```

lbourdois commited on May 20

Commit

0ebfbaf

verified ·

1 Parent(s): 8df8569

Update model card for Malay

Browse files

Files changed (1) hide show

README.md +86 -73

README.md CHANGED Viewed

@@ -1,73 +1,86 @@
----
-language: msa
-license: gemma
-tags:
-  - trimmed
-  - gemma
-  - gemma-3
-base_model: google/gemma-3-1b-it
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-# gemma-3-1b-it-msa-16384
-This model is a **28.31% smaller** version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) optimized for Malay language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/introduction-to-trimming) method.
-This trimmed model should perform similarly to the original model with only **16,384 tokens** and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in Malay were removed from the vocabulary.
-## Model Statistics
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 262,144 tokens | 16,384 tokens | **93.75%** |
-| **Model size** | 999,885,952 params | 716,770,432 params | **28.31%** |
-## Mining Dataset Statistics
-- **Number of texts used for mining**: 200,000 texts
-- **Dataset**: [Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming)
-## Usage
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "lbourdois/gemma-3-1b-it-msa-16384"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-prompt = "Your prompt in Malay."
-messages = [{"role": "user", "content": prompt}]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-generated_ids = model.generate(**model_inputs, max_new_tokens=256)
-output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
-response = tokenizer.decode(output_ids, skip_special_tokens=True)
-print(response)
-```
-## Citation
-#### Gemma 3
-```bibtex
-@misc{gemmateam2025gemma3technicalreport,
-      title={Gemma 3 Technical Report},
-      author={Gemma Team},
-      year={2025},
-      eprint={2503.19786},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2503.19786},
-}
-```

+---
+pipeline_tag: text-generation
+language: msa
+license: gemma
+tags:
+  - trimmed
+library_name: transformers
+base_model: google/gemma-3-1b-it
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+# gemma-3-1b-it-msa-16384
+This model is a **28.31%** smaller version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) optimized for Malay language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming) method.
+This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
+## Model Statistics
+| Metric | Original | Trimmed | Reduction |
+|--------|----------|---------|-----------|
+| **Vocabulary size** | 262,144 tokens | 16,384 tokens | **93.75%** |
+| **Model size** | 999,885,952 params | 716,770,432 params | **28.31%** |
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/gemma-3-1b-it-16384.png)
+## Mining Dataset Statistics
+- **Number of texts used for mining**: 200,000 texts
+- **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming)
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "alphaedge-ai/gemma-3-1b-it-msa-16384"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+prompt = "Your prompt in Malay."
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(**model_inputs, max_new_tokens=256)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
+response = tokenizer.decode(output_ids, skip_special_tokens=True)
+print(response)
+```
+## Citations
+#### Gemma 3
+```bibtex
+@misc{gemmateam2025gemma3technicalreport,
+      title={Gemma 3 Technical Report},
+      author={Gemma Team},
+      year={2025},
+      eprint={2503.19786},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2503.19786},
+}
+```
+#### Trimming blog post
+```
+@misc{hf_blogpost_trimming,
+      title={Introduction to Trimming},
+      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
+      year={2026},
+      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming},
+}
+```
+### License
+This model is derived from [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
+Use of this model is governed by the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
+By using this model, you agree to the Gemma Terms of Use. This model is not affiliated with or endorsed by Google.