Image-Text-to-Text
Transformers
Safetensors
GGUF
English
gemma4
text-generation-inference
unsloth
conversational
4-bit precision
bitsandbytes
Instructions to use rimashussain/gemma4-cubicasa-floorplan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rimashussain/gemma4-cubicasa-floorplan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="rimashussain/gemma4-cubicasa-floorplan") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("rimashussain/gemma4-cubicasa-floorplan") model = AutoModelForMultimodalLM.from_pretrained("rimashussain/gemma4-cubicasa-floorplan") 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 rimashussain/gemma4-cubicasa-floorplan with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="rimashussain/gemma4-cubicasa-floorplan", filename="gemma-4-E4B-it.F16-mmproj.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use rimashussain/gemma4-cubicasa-floorplan with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf rimashussain/gemma4-cubicasa-floorplan:F16 # Run inference directly in the terminal: llama cli -hf rimashussain/gemma4-cubicasa-floorplan:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf rimashussain/gemma4-cubicasa-floorplan:F16 # Run inference directly in the terminal: llama cli -hf rimashussain/gemma4-cubicasa-floorplan:F16
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 rimashussain/gemma4-cubicasa-floorplan:F16 # Run inference directly in the terminal: ./llama-cli -hf rimashussain/gemma4-cubicasa-floorplan:F16
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 rimashussain/gemma4-cubicasa-floorplan:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf rimashussain/gemma4-cubicasa-floorplan:F16
Use Docker
docker model run hf.co/rimashussain/gemma4-cubicasa-floorplan:F16
- LM Studio
- Jan
- vLLM
How to use rimashussain/gemma4-cubicasa-floorplan with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rimashussain/gemma4-cubicasa-floorplan" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rimashussain/gemma4-cubicasa-floorplan", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/rimashussain/gemma4-cubicasa-floorplan:F16
- SGLang
How to use rimashussain/gemma4-cubicasa-floorplan 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 "rimashussain/gemma4-cubicasa-floorplan" \ --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": "rimashussain/gemma4-cubicasa-floorplan", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "rimashussain/gemma4-cubicasa-floorplan" \ --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": "rimashussain/gemma4-cubicasa-floorplan", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use rimashussain/gemma4-cubicasa-floorplan with Ollama:
ollama run hf.co/rimashussain/gemma4-cubicasa-floorplan:F16
- Unsloth Studio
How to use rimashussain/gemma4-cubicasa-floorplan 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 rimashussain/gemma4-cubicasa-floorplan 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 rimashussain/gemma4-cubicasa-floorplan to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rimashussain/gemma4-cubicasa-floorplan to start chatting
- Pi
How to use rimashussain/gemma4-cubicasa-floorplan with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rimashussain/gemma4-cubicasa-floorplan:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "rimashussain/gemma4-cubicasa-floorplan:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use rimashussain/gemma4-cubicasa-floorplan with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rimashussain/gemma4-cubicasa-floorplan:F16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default rimashussain/gemma4-cubicasa-floorplan:F16
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use rimashussain/gemma4-cubicasa-floorplan with Docker Model Runner:
docker model run hf.co/rimashussain/gemma4-cubicasa-floorplan:F16
- Lemonade
How to use rimashussain/gemma4-cubicasa-floorplan with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rimashussain/gemma4-cubicasa-floorplan:F16
Run and chat with the model
lemonade run user.gemma4-cubicasa-floorplan-F16
List all available models
lemonade list
Upload processor
Browse files- chat_template.jinja +37 -334
- processor_config.json +17 -64
- tokenizer.json +2 -2
- tokenizer_config.json +12 -83
chat_template.jinja
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-
{%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
|
| 218 |
-
{#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}
|
| 219 |
-
{%- set prev_nt = namespace(role=None, found=false) -%}
|
| 220 |
-
{%- if loop.index0 > 0 -%}
|
| 221 |
-
{%- for j in range(loop.index0 - 1, -1, -1) -%}
|
| 222 |
-
{%- if not prev_nt.found -%}
|
| 223 |
-
{%- if loop_messages[j]['role'] != 'tool' -%}
|
| 224 |
-
{%- set prev_nt.role = loop_messages[j]['role'] -%}
|
| 225 |
-
{%- set prev_nt.found = true -%}
|
| 226 |
-
{%- endif -%}
|
| 227 |
-
{%- endif -%}
|
| 228 |
-
{%- endfor -%}
|
| 229 |
{%- endif -%}
|
| 230 |
-
{%-
|
| 231 |
-
|
| 232 |
-
|
| 233 |
-
|
| 234 |
-
|
| 235 |
-
{
|
| 236 |
-
|
| 237 |
-
{%- if
|
| 238 |
-
{{
|
| 239 |
-
{%-
|
| 240 |
-
|
| 241 |
-
{%- if
|
| 242 |
-
{
|
| 243 |
-
|
| 244 |
-
|
| 245 |
-
{%- if function['arguments'] is mapping -%}
|
| 246 |
-
{%- set ns_args = namespace(found_first=false) -%}
|
| 247 |
-
{%- for key, value in function['arguments'] | dictsort -%}
|
| 248 |
-
{%- if ns_args.found_first %},{% endif -%}
|
| 249 |
-
{%- set ns_args.found_first = true -%}
|
| 250 |
-
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
| 251 |
-
{%- endfor -%}
|
| 252 |
-
{%- elif function['arguments'] is string -%}
|
| 253 |
-
{{- function['arguments'] -}}
|
| 254 |
-
{%- endif -%}
|
| 255 |
-
{{- '}<tool_call|>' -}}
|
| 256 |
-
{%- endfor -%}
|
| 257 |
-
{%- set ns.prev_message_type = 'tool_call' -%}
|
| 258 |
-
{%- endif -%}
|
| 259 |
-
|
| 260 |
-
{%- set ns_tr_out = namespace(flag=false) -%}
|
| 261 |
-
{%- if message.get('tool_responses') -%}
|
| 262 |
-
{#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
|
| 263 |
-
{%- for tool_response in message['tool_responses'] -%}
|
| 264 |
-
{{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}
|
| 265 |
-
{%- set ns_tr_out.flag = true -%}
|
| 266 |
-
{%- set ns.prev_message_type = 'tool_response' -%}
|
| 267 |
-
{%- endfor -%}
|
| 268 |
-
{%- elif message.get('tool_calls') -%}
|
| 269 |
-
{#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
|
| 270 |
-
{%- set ns_tool_scan = namespace(stopped=false) -%}
|
| 271 |
-
{%- for k in range(loop.index0 + 1, loop_messages | length) -%}
|
| 272 |
-
{%- if ns_tool_scan.stopped -%}
|
| 273 |
-
{%- elif loop_messages[k]['role'] != 'tool' -%}
|
| 274 |
-
{%- set ns_tool_scan.stopped = true -%}
|
| 275 |
-
{%- else -%}
|
| 276 |
-
{%- set follow = loop_messages[k] -%}
|
| 277 |
-
{#- Resolve tool_call_id to function name -#}
|
| 278 |
-
{%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}
|
| 279 |
-
{%- for tc in message['tool_calls'] -%}
|
| 280 |
-
{%- if tc.get('id') == follow.get('tool_call_id') -%}
|
| 281 |
-
{%- set ns_tname.name = tc['function']['name'] -%}
|
| 282 |
-
{%- endif -%}
|
| 283 |
-
{%- endfor -%}
|
| 284 |
-
{#- Handle content as string or content-parts array -#}
|
| 285 |
-
{%- set tool_body = follow.get('content') -%}
|
| 286 |
-
{%- if tool_body is string -%}
|
| 287 |
-
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
|
| 288 |
-
{%- elif tool_body is sequence and tool_body is not string -%}
|
| 289 |
-
{%- set ns_txt = namespace(s='') -%}
|
| 290 |
-
{%- for part in tool_body -%}
|
| 291 |
-
{%- if part.get('type') == 'text' -%}
|
| 292 |
-
{%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}
|
| 293 |
-
{%- endif -%}
|
| 294 |
-
{%- endfor -%}
|
| 295 |
-
{{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
|
| 296 |
-
{%- else -%}
|
| 297 |
-
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
|
| 298 |
-
{%- endif -%}
|
| 299 |
-
{%- set ns_tr_out.flag = true -%}
|
| 300 |
-
{%- set ns.prev_message_type = 'tool_response' -%}
|
| 301 |
-
{%- endif -%}
|
| 302 |
-
{%- endfor -%}
|
| 303 |
-
{%- endif -%}
|
| 304 |
-
|
| 305 |
-
{%- if message['content'] is string -%}
|
| 306 |
-
{%- if role == 'model' -%}
|
| 307 |
-
{{- strip_thinking(message['content']) -}}
|
| 308 |
-
{%- else -%}
|
| 309 |
-
{{- message['content'] | trim -}}
|
| 310 |
-
{%- endif -%}
|
| 311 |
-
{%- elif message['content'] is sequence -%}
|
| 312 |
-
{%- for item in message['content'] -%}
|
| 313 |
-
{%- if item['type'] == 'text' -%}
|
| 314 |
-
{%- if role == 'model' -%}
|
| 315 |
-
{{- strip_thinking(item['text']) -}}
|
| 316 |
-
{%- else -%}
|
| 317 |
-
{{- item['text'] | trim -}}
|
| 318 |
-
{%- endif -%}
|
| 319 |
-
{%- elif item['type'] == 'image' -%}
|
| 320 |
-
{{- '<|image|>' -}}
|
| 321 |
-
{%- set ns.prev_message_type = 'image' -%}
|
| 322 |
-
{%- elif item['type'] == 'audio' -%}
|
| 323 |
-
{{- '<|audio|>' -}}
|
| 324 |
-
{%- set ns.prev_message_type = 'audio' -%}
|
| 325 |
-
{%- elif item['type'] == 'video' -%}
|
| 326 |
-
{{- '<|video|>' -}}
|
| 327 |
-
{%- set ns.prev_message_type = 'video' -%}
|
| 328 |
-
{%- endif -%}
|
| 329 |
-
{%- endfor -%}
|
| 330 |
{%- endif -%}
|
| 331 |
-
|
| 332 |
-
|
| 333 |
-
|
| 334 |
-
{%- elif not (ns_tr_out.flag and not message.get('content')) -%}
|
| 335 |
-
{{- '<turn|>\n' -}}
|
| 336 |
-
{%- endif -%}
|
| 337 |
{%- endif -%}
|
|
|
|
|
|
|
| 338 |
{%- endfor -%}
|
| 339 |
-
|
| 340 |
{%- if add_generation_prompt -%}
|
| 341 |
-
{
|
| 342 |
-
|
| 343 |
-
|
| 344 |
-
{%- endif -%}
|
|
|
|
| 1 |
+
{{ bos_token }}
|
| 2 |
+
{%- if messages[0]['role'] == 'system' -%}
|
| 3 |
+
{%- if messages[0]['content'] is string -%}
|
| 4 |
+
{%- set first_user_prefix = messages[0]['content'] + '
|
|
|
|
|
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|
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|
| 5 |
|
| 6 |
+
' -%}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 7 |
{%- else -%}
|
| 8 |
+
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 9 |
|
| 10 |
+
' -%}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 11 |
{%- endif -%}
|
| 12 |
+
{%- set loop_messages = messages[1:] -%}
|
| 13 |
+
{%- else -%}
|
| 14 |
+
{%- set first_user_prefix = "" -%}
|
| 15 |
+
{%- set loop_messages = messages -%}
|
| 16 |
+
{%- endif -%}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 17 |
{%- for message in loop_messages -%}
|
| 18 |
+
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
|
| 19 |
+
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 20 |
{%- endif -%}
|
| 21 |
+
{%- if (message['role'] == 'assistant') -%}
|
| 22 |
+
{%- set role = "model" -%}
|
| 23 |
+
{%- else -%}
|
| 24 |
+
{%- set role = message['role'] -%}
|
| 25 |
+
{%- endif -%}
|
| 26 |
+
{{ '<start_of_turn>' + role + '
|
| 27 |
+
' + (first_user_prefix if loop.first else "") }}
|
| 28 |
+
{%- if message['content'] is string -%}
|
| 29 |
+
{{ message['content'] | trim }}
|
| 30 |
+
{%- elif message['content'] is iterable -%}
|
| 31 |
+
{%- for item in message['content'] -%}
|
| 32 |
+
{%- if item['type'] == 'image' -%}
|
| 33 |
+
{{ '<start_of_image>' }}
|
| 34 |
+
{%- elif item['type'] == 'text' -%}
|
| 35 |
+
{{ item['text'] | trim }}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 36 |
{%- endif -%}
|
| 37 |
+
{%- endfor -%}
|
| 38 |
+
{%- else -%}
|
| 39 |
+
{{ raise_exception("Invalid content type") }}
|
|
|
|
|
|
|
|
|
|
| 40 |
{%- endif -%}
|
| 41 |
+
{{ '<end_of_turn>
|
| 42 |
+
' }}
|
| 43 |
{%- endfor -%}
|
|
|
|
| 44 |
{%- if add_generation_prompt -%}
|
| 45 |
+
{{'<start_of_turn>model
|
| 46 |
+
'}}
|
| 47 |
+
{%- endif -%}
|
|
|
processor_config.json
CHANGED
|
@@ -1,75 +1,28 @@
|
|
| 1 |
{
|
| 2 |
-
"audio_ms_per_token": 40,
|
| 3 |
-
"audio_seq_length": 750,
|
| 4 |
-
"feature_extractor": {
|
| 5 |
-
"dither": 0.0,
|
| 6 |
-
"feature_extractor_type": "Gemma4AudioFeatureExtractor",
|
| 7 |
-
"feature_size": 128,
|
| 8 |
-
"fft_length": 512,
|
| 9 |
-
"fft_overdrive": false,
|
| 10 |
-
"frame_length": 320,
|
| 11 |
-
"hop_length": 160,
|
| 12 |
-
"input_scale_factor": 1.0,
|
| 13 |
-
"max_frequency": 8000.0,
|
| 14 |
-
"mel_floor": 0.001,
|
| 15 |
-
"min_frequency": 0.0,
|
| 16 |
-
"padding_side": "left",
|
| 17 |
-
"padding_value": 0.0,
|
| 18 |
-
"per_bin_mean": null,
|
| 19 |
-
"per_bin_stddev": null,
|
| 20 |
-
"preemphasis": 0.0,
|
| 21 |
-
"preemphasis_htk_flavor": true,
|
| 22 |
-
"return_attention_mask": true,
|
| 23 |
-
"sampling_rate": 16000
|
| 24 |
-
},
|
| 25 |
"image_processor": {
|
| 26 |
-
"do_convert_rgb":
|
| 27 |
-
"do_normalize": false,
|
| 28 |
-
"do_rescale": true,
|
| 29 |
-
"do_resize": true,
|
| 30 |
-
"image_mean": [
|
| 31 |
-
0.0,
|
| 32 |
-
0.0,
|
| 33 |
-
0.0
|
| 34 |
-
],
|
| 35 |
-
"image_processor_type": "Gemma4ImageProcessor",
|
| 36 |
-
"image_seq_length": 280,
|
| 37 |
-
"image_std": [
|
| 38 |
-
1.0,
|
| 39 |
-
1.0,
|
| 40 |
-
1.0
|
| 41 |
-
],
|
| 42 |
-
"max_soft_tokens": 280,
|
| 43 |
-
"patch_size": 16,
|
| 44 |
-
"pooling_kernel_size": 3,
|
| 45 |
-
"resample": 3,
|
| 46 |
-
"rescale_factor": 0.00392156862745098
|
| 47 |
-
},
|
| 48 |
-
"image_seq_length": 280,
|
| 49 |
-
"processor_class": "Gemma4Processor",
|
| 50 |
-
"video_processor": {
|
| 51 |
-
"do_convert_rgb": true,
|
| 52 |
"do_normalize": true,
|
| 53 |
"do_rescale": true,
|
| 54 |
"do_resize": true,
|
| 55 |
-
"do_sample_frames": true,
|
| 56 |
"image_mean": [
|
| 57 |
-
0.
|
| 58 |
-
0.
|
| 59 |
-
0.
|
| 60 |
],
|
|
|
|
|
|
|
| 61 |
"image_std": [
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
|
| 65 |
],
|
| 66 |
-
"
|
| 67 |
-
"num_frames": 32,
|
| 68 |
-
"patch_size": 16,
|
| 69 |
-
"pooling_kernel_size": 3,
|
| 70 |
-
"resample": 3,
|
| 71 |
"rescale_factor": 0.00392156862745098,
|
| 72 |
-
"
|
| 73 |
-
|
| 74 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 75 |
}
|
|
|
|
| 1 |
{
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
"image_processor": {
|
| 3 |
+
"do_convert_rgb": null,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
"do_normalize": true,
|
| 5 |
"do_rescale": true,
|
| 6 |
"do_resize": true,
|
|
|
|
| 7 |
"image_mean": [
|
| 8 |
+
0.5,
|
| 9 |
+
0.5,
|
| 10 |
+
0.5
|
| 11 |
],
|
| 12 |
+
"image_processor_type": "Gemma3ImageProcessor",
|
| 13 |
+
"image_seq_length": 256,
|
| 14 |
"image_std": [
|
| 15 |
+
0.5,
|
| 16 |
+
0.5,
|
| 17 |
+
0.5
|
| 18 |
],
|
| 19 |
+
"resample": 2,
|
|
|
|
|
|
|
|
|
|
|
|
|
| 20 |
"rescale_factor": 0.00392156862745098,
|
| 21 |
+
"size": {
|
| 22 |
+
"height": 896,
|
| 23 |
+
"width": 896
|
| 24 |
+
}
|
| 25 |
+
},
|
| 26 |
+
"image_seq_length": 256,
|
| 27 |
+
"processor_class": "Gemma3Processor"
|
| 28 |
}
|
tokenizer.json
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f4708757955e49e5b23494815a523ffa5bdd0a7b67c09d16a093f6151245ec5b
|
| 3 |
+
size 33384665
|
tokenizer_config.json
CHANGED
|
@@ -1,95 +1,24 @@
|
|
| 1 |
{
|
| 2 |
-
"audio_token": "<|audio|>",
|
| 3 |
"backend": "tokenizers",
|
| 4 |
-
"
|
| 5 |
-
"boi_token": "<|image>",
|
| 6 |
"bos_token": "<bos>",
|
| 7 |
-
"
|
| 8 |
-
"
|
| 9 |
-
"eoi_token": "<image|>",
|
| 10 |
"eos_token": "<eos>",
|
| 11 |
-
"
|
| 12 |
-
"escape_token": "<|\"|>",
|
| 13 |
-
"etc_token": "<tool_call|>",
|
| 14 |
-
"etd_token": "<tool|>",
|
| 15 |
-
"etr_token": "<tool_response|>",
|
| 16 |
-
"extra_special_tokens": [
|
| 17 |
-
"<|video|>"
|
| 18 |
-
],
|
| 19 |
-
"image_token": "<|image|>",
|
| 20 |
"is_local": false,
|
| 21 |
"mask_token": "<mask>",
|
| 22 |
"model_max_length": 1000000000000000019884624838656,
|
| 23 |
"model_specific_special_tokens": {
|
| 24 |
-
"
|
| 25 |
-
"
|
| 26 |
-
"
|
| 27 |
-
"eoa_token": "<audio|>",
|
| 28 |
-
"eoc_token": "<channel|>",
|
| 29 |
-
"eoi_token": "<image|>",
|
| 30 |
-
"eot_token": "<turn|>",
|
| 31 |
-
"escape_token": "<|\"|>",
|
| 32 |
-
"etc_token": "<tool_call|>",
|
| 33 |
-
"etd_token": "<tool|>",
|
| 34 |
-
"etr_token": "<tool_response|>",
|
| 35 |
-
"image_token": "<|image|>",
|
| 36 |
-
"soc_token": "<|channel>",
|
| 37 |
-
"sot_token": "<|turn>",
|
| 38 |
-
"stc_token": "<|tool_call>",
|
| 39 |
-
"std_token": "<|tool>",
|
| 40 |
-
"str_token": "<|tool_response>",
|
| 41 |
-
"think_token": "<|think|>"
|
| 42 |
},
|
| 43 |
"pad_token": "<pad>",
|
| 44 |
-
"
|
| 45 |
-
"
|
| 46 |
-
"
|
| 47 |
-
"properties": {
|
| 48 |
-
"content": {
|
| 49 |
-
"type": "string"
|
| 50 |
-
},
|
| 51 |
-
"role": {
|
| 52 |
-
"const": "assistant"
|
| 53 |
-
},
|
| 54 |
-
"thinking": {
|
| 55 |
-
"type": "string"
|
| 56 |
-
},
|
| 57 |
-
"tool_calls": {
|
| 58 |
-
"items": {
|
| 59 |
-
"properties": {
|
| 60 |
-
"function": {
|
| 61 |
-
"properties": {
|
| 62 |
-
"arguments": {
|
| 63 |
-
"additionalProperties": {},
|
| 64 |
-
"type": "object",
|
| 65 |
-
"x-parser": "gemma4-tool-call"
|
| 66 |
-
},
|
| 67 |
-
"name": {
|
| 68 |
-
"type": "string"
|
| 69 |
-
}
|
| 70 |
-
},
|
| 71 |
-
"type": "object",
|
| 72 |
-
"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
|
| 73 |
-
},
|
| 74 |
-
"type": {
|
| 75 |
-
"const": "function"
|
| 76 |
-
}
|
| 77 |
-
},
|
| 78 |
-
"type": "object"
|
| 79 |
-
},
|
| 80 |
-
"type": "array",
|
| 81 |
-
"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
|
| 82 |
-
}
|
| 83 |
-
},
|
| 84 |
-
"type": "object",
|
| 85 |
-
"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
|
| 86 |
-
},
|
| 87 |
-
"soc_token": "<|channel>",
|
| 88 |
-
"sot_token": "<|turn>",
|
| 89 |
-
"stc_token": "<|tool_call>",
|
| 90 |
-
"std_token": "<|tool>",
|
| 91 |
-
"str_token": "<|tool_response>",
|
| 92 |
-
"think_token": "<|think|>",
|
| 93 |
"tokenizer_class": "GemmaTokenizer",
|
| 94 |
-
"unk_token": "<unk>"
|
|
|
|
| 95 |
}
|
|
|
|
| 1 |
{
|
|
|
|
| 2 |
"backend": "tokenizers",
|
| 3 |
+
"boi_token": "<start_of_image>",
|
|
|
|
| 4 |
"bos_token": "<bos>",
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eoi_token": "<end_of_image>",
|
|
|
|
| 7 |
"eos_token": "<eos>",
|
| 8 |
+
"image_token": "<image_soft_token>",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
"is_local": false,
|
| 10 |
"mask_token": "<mask>",
|
| 11 |
"model_max_length": 1000000000000000019884624838656,
|
| 12 |
"model_specific_special_tokens": {
|
| 13 |
+
"boi_token": "<start_of_image>",
|
| 14 |
+
"eoi_token": "<end_of_image>",
|
| 15 |
+
"image_token": "<image_soft_token>"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 16 |
},
|
| 17 |
"pad_token": "<pad>",
|
| 18 |
+
"processor_class": "Gemma3Processor",
|
| 19 |
+
"sp_model_kwargs": null,
|
| 20 |
+
"spaces_between_special_tokens": false,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 21 |
"tokenizer_class": "GemmaTokenizer",
|
| 22 |
+
"unk_token": "<unk>",
|
| 23 |
+
"use_default_system_prompt": false
|
| 24 |
}
|