Instructions to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with Transformers:

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

pipe = pipeline("text-generation", model="AMKCode/gemma-2-2b-it-q4f32_1-MLC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AMKCode/gemma-2-2b-it-q4f32_1-MLC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMKCode/gemma-2-2b-it-q4f32_1-MLC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AMKCode/gemma-2-2b-it-q4f32_1-MLC

SGLang

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC 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 "AMKCode/gemma-2-2b-it-q4f32_1-MLC" \
    --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": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"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 "AMKCode/gemma-2-2b-it-q4f32_1-MLC" \
        --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": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with Docker Model Runner:
```
docker model run hf.co/AMKCode/gemma-2-2b-it-q4f32_1-MLC
```

gemma-2-2b-it-q4f32_1-MLC

File size: 2,092 Bytes

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{
  "version": "0.1.0",
  "model_type": "gemma2",
  "quantization": "q4f32_1",
  "model_config": {
    "hidden_size": 2304,
    "intermediate_size": 9216,
    "attention_bias": false,
    "num_attention_heads": 8,
    "num_key_value_heads": 4,
    "head_dim": 256,
    "num_hidden_layers": 26,
    "rms_norm_eps": 1e-06,
    "vocab_size": 256000,
    "hidden_activation": "gelu_pytorch_tanh",
    "position_embedding_base": 10000.0,
    "context_window_size": 4096,
    "prefill_chunk_size": 4096,
    "tensor_parallel_shards": 1,
    "max_batch_size": 128,
    "attn_logit_softcapping": 50.0,
    "final_logit_softcapping": 30.0,
    "query_pre_attn_scalar": 256,
    "sliding_window": 4096
  },
  "vocab_size": 256000,
  "context_window_size": 4096,
  "sliding_window_size": -1,
  "prefill_chunk_size": 4096,
  "attention_sink_size": -1,
  "tensor_parallel_shards": 1,
  "pipeline_parallel_stages": 1,
  "temperature": 1.0,
  "presence_penalty": 0.0,
  "frequency_penalty": 0.0,
  "repetition_penalty": 1.0,
  "top_p": 1.0,
  "tokenizer_files": [
    "tokenizer.model",
    "tokenizer.json",
    "tokenizer_config.json"
  ],
  "tokenizer_info": {
    "token_postproc_method": "byte_fallback",
    "prepend_space_in_encode": false,
    "strip_space_in_decode": false
  },
  "conv_template": {
    "name": "gemma_instruction",
    "system_template": "{system_message}",
    "system_message": "",
    "system_prefix_token_ids": [
      2
    ],
    "add_role_after_system_message": true,
    "roles": {
      "user": "<start_of_turn>user",
      "assistant": "<start_of_turn>model"
    },
    "role_templates": {
      "user": "{user_message}",
      "assistant": "{assistant_message}",
      "tool": "{tool_message}"
    },
    "messages": [],
    "seps": [
      "<end_of_turn>\n"
    ],
    "role_content_sep": "\n",
    "role_empty_sep": "\n",
    "stop_str": [
      "<end_of_turn>"
    ],
    "stop_token_ids": [
      1,
      107
    ],
    "function_string": "",
    "use_function_calling": false
  },
  "pad_token_id": 0,
  "bos_token_id": 2,
  "eos_token_id": [
    1,
    107
  ]
}