Instructions to use daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi with Transformers:

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

pipe = pipeline("text-generation", model="daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi")
model = AutoModelForCausalLM.from_pretrained("daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi")
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 daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi

SGLang

How to use daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi 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 "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi" \
    --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": "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi",
		"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 "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi" \
        --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": "daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi with Docker Model Runner:
```
docker model run hf.co/daniazie/GemmaX2-28-2B-v0.1_sft_CCAligned_10000_sea_bnb-bi
```

daniazie commited on Dec 7, 2025

Commit

209c6a9

verified ·

1 Parent(s): 1f56328

Upload Gemma2ForCausalLM

Browse files

Files changed (3) hide show

config.json +75 -0
generation_config.json +10 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,75 @@

+{
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "float16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "layer_types": [
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "bfloat16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,10 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "4.56.2"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 2224764878