Text Generation
Transformers
Safetensors
GGUF
Korean
gemma4
image-text-to-text
gemma
korean
roleplay
mud
lore
llama.cpp
lmstudio
conversational
Instructions to use sangwon1472/gemma4-e2b-mud with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sangwon1472/gemma4-e2b-mud with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sangwon1472/gemma4-e2b-mud") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("sangwon1472/gemma4-e2b-mud") model = AutoModelForImageTextToText.from_pretrained("sangwon1472/gemma4-e2b-mud") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use sangwon1472/gemma4-e2b-mud with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sangwon1472/gemma4-e2b-mud", filename="gemma4-e2b-mud-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sangwon1472/gemma4-e2b-mud with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
Use Docker
docker model run hf.co/sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
- LM Studio
- Jan
- vLLM
How to use sangwon1472/gemma4-e2b-mud with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sangwon1472/gemma4-e2b-mud" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sangwon1472/gemma4-e2b-mud", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
- SGLang
How to use sangwon1472/gemma4-e2b-mud 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 "sangwon1472/gemma4-e2b-mud" \ --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": "sangwon1472/gemma4-e2b-mud", "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 "sangwon1472/gemma4-e2b-mud" \ --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": "sangwon1472/gemma4-e2b-mud", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use sangwon1472/gemma4-e2b-mud with Ollama:
ollama run hf.co/sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
- Unsloth Studio new
How to use sangwon1472/gemma4-e2b-mud with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sangwon1472/gemma4-e2b-mud to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sangwon1472/gemma4-e2b-mud to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sangwon1472/gemma4-e2b-mud to start chatting
- Docker Model Runner
How to use sangwon1472/gemma4-e2b-mud with Docker Model Runner:
docker model run hf.co/sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
- Lemonade
How to use sangwon1472/gemma4-e2b-mud with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sangwon1472/gemma4-e2b-mud:UD-Q4_K_M
Run and chat with the model
lemonade run user.gemma4-e2b-mud-UD-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- chat_template.jinja +44 -0
- config.json +192 -0
- model-00001-of-00005.safetensors +3 -0
- model-00002-of-00005.safetensors +3 -0
- model-00003-of-00005.safetensors +3 -0
- model-00004-of-00005.safetensors +3 -0
- model-00005-of-00005.safetensors +3 -0
- model.safetensors.index.json +0 -0
- processor_config.json +75 -0
- tokenizer.json +3 -0
- tokenizer_config.json +96 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
ADDED
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@@ -0,0 +1,44 @@
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| 1 |
+
{{ bos_token }}{%- if messages[0]['role'] == 'system' -%}
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| 2 |
+
{%- set first_user_prefix = messages[0]['content'] + '
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| 3 |
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| 4 |
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' -%}
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| 5 |
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{%- set loop_messages = messages[1:] -%}
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| 6 |
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{%- else -%}
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| 7 |
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{%- set first_user_prefix = "" -%}
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| 8 |
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{%- set loop_messages = messages -%}
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| 9 |
+
{%- endif -%}
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| 10 |
+
{%- for message in loop_messages -%}
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| 11 |
+
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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| 12 |
+
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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| 13 |
+
{%- endif -%}
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| 14 |
+
{%- if (message['role'] == 'assistant') -%}
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| 15 |
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{%- set role = "model" -%}
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| 16 |
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{%- else -%}
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| 17 |
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{%- set role = message['role'] -%}
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| 18 |
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{%- endif -%}
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| 19 |
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{{ '<|turn>' + role + '
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| 20 |
+
' + (first_user_prefix if loop.first else "") }}
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| 21 |
+
{%- if message['content'] is string -%}
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| 22 |
+
{{ message['content'] | trim }}
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| 23 |
+
{%- elif message['content'] is iterable -%}
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| 24 |
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{%- for item in message['content'] -%}
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| 25 |
+
{%- if item['type'] == 'audio' -%}
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| 26 |
+
{{ '<|audio|>' }}
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| 27 |
+
{%- elif item['type'] == 'image' -%}
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| 28 |
+
{{ '<|image|>' }}
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| 29 |
+
{%- elif item['type'] == 'video' -%}
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| 30 |
+
{{ '<|video|>' }}
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| 31 |
+
{%- elif item['type'] == 'text' -%}
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| 32 |
+
{{ item['text'] | trim }}
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| 33 |
+
{%- endif -%}
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| 34 |
+
{%- endfor -%}
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| 35 |
+
{%- else -%}
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| 36 |
+
{{ raise_exception("Invalid content type") }}
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| 37 |
+
{%- endif -%}
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| 38 |
+
{{ '<turn|>
|
| 39 |
+
' }}
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| 40 |
+
{%- endfor -%}
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| 41 |
+
{%- if add_generation_prompt -%}
|
| 42 |
+
{{'<|turn>model
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| 43 |
+
'}}
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| 44 |
+
{%- endif -%}
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config.json
ADDED
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@@ -0,0 +1,192 @@
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| 1 |
+
{
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| 2 |
+
"architectures": [
|
| 3 |
+
"Gemma4ForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"audio_config": {
|
| 6 |
+
"_name_or_path": "",
|
| 7 |
+
"architectures": null,
|
| 8 |
+
"attention_chunk_size": 12,
|
| 9 |
+
"attention_context_left": 13,
|
| 10 |
+
"attention_context_right": 0,
|
| 11 |
+
"attention_invalid_logits_value": -1000000000.0,
|
| 12 |
+
"attention_logit_cap": 50.0,
|
| 13 |
+
"chunk_size_feed_forward": 0,
|
| 14 |
+
"conv_kernel_size": 5,
|
| 15 |
+
"torch_dtype": "float16",
|
| 16 |
+
"gradient_clipping": 10000000000.0,
|
| 17 |
+
"hidden_act": "silu",
|
| 18 |
+
"hidden_size": 1024,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "LABEL_0",
|
| 21 |
+
"1": "LABEL_1"
|
| 22 |
+
},
|
| 23 |
+
"initializer_range": 0.02,
|
| 24 |
+
"is_encoder_decoder": false,
|
| 25 |
+
"label2id": {
|
| 26 |
+
"LABEL_0": 0,
|
| 27 |
+
"LABEL_1": 1
|
| 28 |
+
},
|
| 29 |
+
"model_type": "gemma4_audio",
|
| 30 |
+
"num_attention_heads": 8,
|
| 31 |
+
"num_hidden_layers": 12,
|
| 32 |
+
"output_attentions": false,
|
| 33 |
+
"output_hidden_states": false,
|
| 34 |
+
"output_proj_dims": 1536,
|
| 35 |
+
"problem_type": null,
|
| 36 |
+
"residual_weight": 0.5,
|
| 37 |
+
"return_dict": true,
|
| 38 |
+
"rms_norm_eps": 1e-06,
|
| 39 |
+
"subsampling_conv_channels": [
|
| 40 |
+
128,
|
| 41 |
+
32
|
| 42 |
+
],
|
| 43 |
+
"use_clipped_linears": true
|
| 44 |
+
},
|
| 45 |
+
"audio_token_id": 258881,
|
| 46 |
+
"boa_token_id": 256000,
|
| 47 |
+
"boi_token_id": 255999,
|
| 48 |
+
"torch_dtype": "float16",
|
| 49 |
+
"eoa_token_id": 258883,
|
| 50 |
+
"eoa_token_index": 258883,
|
| 51 |
+
"eoi_token_id": 258882,
|
| 52 |
+
"eos_token_id": 106,
|
| 53 |
+
"image_token_id": 258880,
|
| 54 |
+
"initializer_range": 0.02,
|
| 55 |
+
"model_name": "unsloth/gemma-4-E2B-it",
|
| 56 |
+
"model_type": "gemma4",
|
| 57 |
+
"pad_token_id": 0,
|
| 58 |
+
"text_config": {
|
| 59 |
+
"attention_bias": false,
|
| 60 |
+
"attention_dropout": 0.0,
|
| 61 |
+
"attention_k_eq_v": false,
|
| 62 |
+
"bos_token_id": 2,
|
| 63 |
+
"torch_dtype": "float16",
|
| 64 |
+
"enable_moe_block": false,
|
| 65 |
+
"eos_token_id": 1,
|
| 66 |
+
"expert_intermediate_size": null,
|
| 67 |
+
"final_logit_softcapping": 30.0,
|
| 68 |
+
"global_head_dim": 512,
|
| 69 |
+
"head_dim": 256,
|
| 70 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 71 |
+
"hidden_size": 1536,
|
| 72 |
+
"hidden_size_per_layer_input": 256,
|
| 73 |
+
"initializer_range": 0.02,
|
| 74 |
+
"intermediate_size": 6144,
|
| 75 |
+
"layer_types": [
|
| 76 |
+
"sliding_attention",
|
| 77 |
+
"sliding_attention",
|
| 78 |
+
"sliding_attention",
|
| 79 |
+
"sliding_attention",
|
| 80 |
+
"full_attention",
|
| 81 |
+
"sliding_attention",
|
| 82 |
+
"sliding_attention",
|
| 83 |
+
"sliding_attention",
|
| 84 |
+
"sliding_attention",
|
| 85 |
+
"full_attention",
|
| 86 |
+
"sliding_attention",
|
| 87 |
+
"sliding_attention",
|
| 88 |
+
"sliding_attention",
|
| 89 |
+
"sliding_attention",
|
| 90 |
+
"full_attention",
|
| 91 |
+
"sliding_attention",
|
| 92 |
+
"sliding_attention",
|
| 93 |
+
"sliding_attention",
|
| 94 |
+
"sliding_attention",
|
| 95 |
+
"full_attention",
|
| 96 |
+
"sliding_attention",
|
| 97 |
+
"sliding_attention",
|
| 98 |
+
"sliding_attention",
|
| 99 |
+
"sliding_attention",
|
| 100 |
+
"full_attention",
|
| 101 |
+
"sliding_attention",
|
| 102 |
+
"sliding_attention",
|
| 103 |
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| 95 |
+
"chat_template": "{{ bos_token }}{%- if messages[0]['role'] == 'system' -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n {%- set first_user_prefix = \"\" -%}\n {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif -%}\n {%- if (message['role'] == 'assistant') -%}\n {%- set role = \"model\" -%}\n {%- else -%}\n {%- set role = message['role'] -%}\n {%- endif -%}\n {{ '<|turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n {%- if message['content'] is string -%}\n {{ message['content'] | trim }}\n {%- elif message['content'] is iterable -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'audio' -%}\n {{ '<|audio|>' }}\n {%- elif item['type'] == 'image' -%}\n {{ '<|image|>' }}\n {%- elif item['type'] == 'video' -%}\n {{ '<|video|>' }}\n {%- elif item['type'] == 'text' -%}\n {{ item['text'] | trim }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ raise_exception(\"Invalid content type\") }}\n {%- endif -%}\n {{ '<turn|>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{'<|turn>model\n'}}\n{%- endif -%}\n"
|
| 96 |
+
}
|