Image-Text-to-Text
Transformers
Safetensors
lfm2_vl
satellite
geospatial
vision-language
lfm
liquid-ai
earth-observation
multi-image
conversational
Instructions to use NuTonic/lspace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NuTonic/lspace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="NuTonic/lspace") 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("NuTonic/lspace") model = AutoModelForImageTextToText.from_pretrained("NuTonic/lspace") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NuTonic/lspace with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NuTonic/lspace" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NuTonic/lspace", "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/NuTonic/lspace
- SGLang
How to use NuTonic/lspace 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 "NuTonic/lspace" \ --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": "NuTonic/lspace", "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 "NuTonic/lspace" \ --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": "NuTonic/lspace", "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" } } ] } ] }' - Docker Model Runner
How to use NuTonic/lspace with Docker Model Runner:
docker model run hf.co/NuTonic/lspace
Upload folder using huggingface_hub
Browse files- README.md +17 -0
- chat_template.jinja +92 -0
- config.json +105 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- processor_config.json +39 -0
- tokenizer.json +0 -0
- tokenizer_config.json +29 -0
README.md
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---
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library_name: transformers
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tags:
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- vision-language
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- satellite
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- geospatial
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- liquid-ai
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- lfm
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base_model: LiquidAI/LFM2.5-VL-450M
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---
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# NuTonic/lspace
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Fine-tuned from `LiquidAI/LFM2.5-VL-450M` using the NU:TONIC satellite VLM SFT mix
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(`train/run_sat_vl_sft_e2e.py`): single LEAP run on main + task + Firewatch Parquet mix.
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Training stack: LEAP `vlm_sft` in this repo's `refs/leap-finetune-main`.
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chat_template.jinja
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{{- bos_token -}}
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{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
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{%- macro format_arg_value(arg_value) -%}
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{%- if arg_value is string -%}
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{{- '"' + arg_value + '"' -}}
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{%- elif arg_value is mapping -%}
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{{- arg_value | tojson -}}
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{%- else -%}
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{{- arg_value | string -}}
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{%- endif -%}
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{%- endmacro -%}
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{%- macro parse_content(content) -%}
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{%- if content is string -%}
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{{- content -}}
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{%- else -%}
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{%- set _ns = namespace(result="") -%}
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{%- for item in content -%}
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{%- if item.type == "image" -%}
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{%- set _ns.result = _ns.result + "<image>" -%}
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{%- elif item.type == "text" -%}
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{%- set _ns.result = _ns.result + item.text -%}
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{%- else -%}
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{%- set _ns.result = _ns.result + item | tojson -%}
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{%- endif -%}
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{%- endfor -%}
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{{- _ns.result -}}
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{%- endif -%}
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{%- endmacro -%}
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{%- macro render_tool_calls(tool_calls) -%}
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{%- set tool_calls_ns = namespace(tool_calls=[]) -%}
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{%- for tool_call in tool_calls -%}
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{%- set func_name = tool_call.function.name -%}
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{%- set func_args = tool_call.function.arguments -%}
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{%- set args_ns = namespace(arg_strings=[]) -%}
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{%- for arg_name, arg_value in func_args.items() -%}
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{%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
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{%- endfor -%}
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{%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
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{%- endfor -%}
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{{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
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{%- endmacro -%}
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{%- set ns = namespace(system_prompt="", last_assistant_index=-1) -%}
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{%- if messages[0].role == "system" -%}
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{%- if messages[0].content is defined -%}
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{%- set ns.system_prompt = parse_content(messages[0].content) -%}
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{%- endif -%}
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{%- set messages = messages[1:] -%}
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{%- endif -%}
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{%- if tools -%}
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{%- set ns.system_prompt = ns.system_prompt + ("\n\n" if ns.system_prompt else "") + "Today's date: " + strftime_now("%Y-%m-%d") + "\n\nList of tools: " + (tools | tojson) -%}
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{%- endif -%}
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{%- if ns.system_prompt -%}
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{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- for message in messages -%}
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{%- if message.role == "assistant" -%}
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{%- set ns.last_assistant_index = loop.index0 -%}
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{%- endif -%}
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{%- endfor -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message.role + "\n" -}}
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{%- if message.role == "assistant" -%}
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{%- generation -%}
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{%- if message.thinking is defined and (keep_past_thinking or loop.index0 == ns.last_assistant_index) -%}
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{{- "<think>" + message.thinking + "</think>" -}}
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{%- endif -%}
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{%- if message.tool_calls is defined -%}
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{{- render_tool_calls(message.tool_calls) -}}
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{%- endif -%}
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{%- if message.content is defined -%}
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{%- set content = parse_content(message.content) -%}
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{%- if not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
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{%- if "</think>" in content -%}
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{%- set content = content.split("</think>")[-1] | trim -%}
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{%- endif -%}
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{%- endif -%}
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{{- content + ("" if (continue_final_message and loop.last) else "<|im_end|>\n") -}}
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{%- endif -%}
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{%- endgeneration -%}
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{%- else %}
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{%- if message.content is defined -%}
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{{- parse_content(message.content) + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- endif %}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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{%- endif -%}
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config.json
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{
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"architectures": [
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"Lfm2VlForConditionalGeneration"
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],
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| 5 |
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"bos_token_id": 1,
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| 6 |
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"do_image_splitting": true,
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| 7 |
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"downsample_factor": 2,
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| 8 |
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"dtype": "bfloat16",
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| 9 |
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"encoder_patch_size": 16,
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| 10 |
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"eos_token_id": 7,
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| 11 |
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"image_token_id": 396,
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| 12 |
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"max_image_tokens": 256,
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| 13 |
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"max_pixels_tolerance": 2.0,
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| 14 |
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"max_tiles": 10,
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| 15 |
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"min_image_tokens": 64,
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| 16 |
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"min_tiles": 2,
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| 17 |
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"model_type": "lfm2_vl",
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| 18 |
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"pad_token_id": 0,
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| 19 |
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"projector_bias": true,
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| 20 |
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"projector_hidden_act": "gelu",
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| 21 |
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"projector_hidden_size": 2048,
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| 22 |
+
"projector_use_layernorm": false,
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| 23 |
+
"text_config": {
|
| 24 |
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"_name_or_path": "LiquidAI/LFM2-350M",
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| 25 |
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"architectures": [
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| 26 |
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"Lfm2ForCausalLM"
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],
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| 28 |
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"block_auto_adjust_ff_dim": true,
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| 29 |
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"block_dim": 1024,
|
| 30 |
+
"block_ff_dim": 6656,
|
| 31 |
+
"block_ffn_dim_multiplier": 1.0,
|
| 32 |
+
"block_mlp_init_scale": 1.0,
|
| 33 |
+
"block_multiple_of": 256,
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| 34 |
+
"block_norm_eps": 1e-05,
|
| 35 |
+
"block_out_init_scale": 1.0,
|
| 36 |
+
"block_use_swiglu": true,
|
| 37 |
+
"block_use_xavier_init": true,
|
| 38 |
+
"bos_token_id": 1,
|
| 39 |
+
"conv_L_cache": 3,
|
| 40 |
+
"conv_bias": false,
|
| 41 |
+
"conv_dim": 1024,
|
| 42 |
+
"conv_dim_out": 1024,
|
| 43 |
+
"conv_use_xavier_init": true,
|
| 44 |
+
"dtype": "bfloat16",
|
| 45 |
+
"eos_token_id": 7,
|
| 46 |
+
"hidden_size": 1024,
|
| 47 |
+
"initializer_range": 0.02,
|
| 48 |
+
"intermediate_size": 6656,
|
| 49 |
+
"layer_types": [
|
| 50 |
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"conv",
|
| 51 |
+
"conv",
|
| 52 |
+
"full_attention",
|
| 53 |
+
"conv",
|
| 54 |
+
"conv",
|
| 55 |
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"full_attention",
|
| 56 |
+
"conv",
|
| 57 |
+
"conv",
|
| 58 |
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"full_attention",
|
| 59 |
+
"conv",
|
| 60 |
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"full_attention",
|
| 61 |
+
"conv",
|
| 62 |
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"full_attention",
|
| 63 |
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"conv",
|
| 64 |
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"full_attention",
|
| 65 |
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"conv"
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| 66 |
+
],
|
| 67 |
+
"max_position_embeddings": 128000,
|
| 68 |
+
"model_type": "lfm2",
|
| 69 |
+
"norm_eps": 1e-05,
|
| 70 |
+
"num_attention_heads": 16,
|
| 71 |
+
"num_heads": 16,
|
| 72 |
+
"num_hidden_layers": 16,
|
| 73 |
+
"num_key_value_heads": 8,
|
| 74 |
+
"pad_token_id": 0,
|
| 75 |
+
"rope_parameters": {
|
| 76 |
+
"rope_theta": 1000000.0,
|
| 77 |
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"rope_type": "default"
|
| 78 |
+
},
|
| 79 |
+
"tie_word_embeddings": true,
|
| 80 |
+
"use_cache": true,
|
| 81 |
+
"use_pos_enc": true,
|
| 82 |
+
"vocab_size": 65536
|
| 83 |
+
},
|
| 84 |
+
"tie_word_embeddings": true,
|
| 85 |
+
"tile_size": 512,
|
| 86 |
+
"transformers_version": "5.2.0",
|
| 87 |
+
"use_cache": false,
|
| 88 |
+
"use_image_special_tokens": true,
|
| 89 |
+
"use_thumbnail": true,
|
| 90 |
+
"vision_config": {
|
| 91 |
+
"attention_dropout": 0.0,
|
| 92 |
+
"dtype": "bfloat16",
|
| 93 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 94 |
+
"hidden_size": 768,
|
| 95 |
+
"intermediate_size": 3072,
|
| 96 |
+
"layer_norm_eps": 1e-06,
|
| 97 |
+
"model_type": "siglip2_vision_model",
|
| 98 |
+
"num_attention_heads": 12,
|
| 99 |
+
"num_channels": 3,
|
| 100 |
+
"num_hidden_layers": 12,
|
| 101 |
+
"num_patches": 256,
|
| 102 |
+
"patch_size": 16,
|
| 103 |
+
"vision_use_head": false
|
| 104 |
+
}
|
| 105 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
7,
|
| 6 |
+
7
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 0,
|
| 9 |
+
"transformers_version": "5.2.0"
|
| 10 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7e9ae0b2225c8755eb68924aa97f81c0826678f77f7832aa81c8398f5439cf5c
|
| 3 |
+
size 897484568
|
processor_config.json
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"image_processor": {
|
| 3 |
+
"data_format": "channels_first",
|
| 4 |
+
"do_image_splitting": true,
|
| 5 |
+
"do_normalize": true,
|
| 6 |
+
"do_pad": true,
|
| 7 |
+
"do_rescale": true,
|
| 8 |
+
"do_resize": true,
|
| 9 |
+
"downsample_factor": 2,
|
| 10 |
+
"encoder_patch_size": 16,
|
| 11 |
+
"image_mean": [
|
| 12 |
+
0.5,
|
| 13 |
+
0.5,
|
| 14 |
+
0.5
|
| 15 |
+
],
|
| 16 |
+
"image_processor_type": "Lfm2VlImageProcessorFast",
|
| 17 |
+
"image_std": [
|
| 18 |
+
0.5,
|
| 19 |
+
0.5,
|
| 20 |
+
0.5
|
| 21 |
+
],
|
| 22 |
+
"max_image_tokens": 256,
|
| 23 |
+
"max_num_patches": 1024,
|
| 24 |
+
"max_pixels_tolerance": 2.0,
|
| 25 |
+
"max_tiles": 10,
|
| 26 |
+
"min_image_tokens": 64,
|
| 27 |
+
"min_tiles": 2,
|
| 28 |
+
"resample": 3,
|
| 29 |
+
"rescale_factor": 0.00392156862745098,
|
| 30 |
+
"return_row_col_info": true,
|
| 31 |
+
"size": {
|
| 32 |
+
"height": 512,
|
| 33 |
+
"width": 512
|
| 34 |
+
},
|
| 35 |
+
"tile_size": 512,
|
| 36 |
+
"use_thumbnail": true
|
| 37 |
+
},
|
| 38 |
+
"processor_class": "Lfm2VlProcessor"
|
| 39 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"bos_token": "<|startoftext|>",
|
| 4 |
+
"clean_up_tokenization_spaces": true,
|
| 5 |
+
"do_image_splitting": true,
|
| 6 |
+
"eos_token": "<|im_end|>",
|
| 7 |
+
"extra_special_tokens": [],
|
| 8 |
+
"image_end_token": "<|image_end|>",
|
| 9 |
+
"image_start_token": "<|image_start|>",
|
| 10 |
+
"image_thumbnail": "<|img_thumbnail|>",
|
| 11 |
+
"image_token": "<image>",
|
| 12 |
+
"is_local": false,
|
| 13 |
+
"legacy": false,
|
| 14 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 15 |
+
"model_specific_special_tokens": {
|
| 16 |
+
"image_end_token": "<|image_end|>",
|
| 17 |
+
"image_start_token": "<|image_start|>",
|
| 18 |
+
"image_token": "<image>"
|
| 19 |
+
},
|
| 20 |
+
"pad_token": "<|pad|>",
|
| 21 |
+
"processor_class": "Lfm2VlProcessor",
|
| 22 |
+
"resample": 3,
|
| 23 |
+
"return_token_type_ids": false,
|
| 24 |
+
"sp_model_kwargs": {},
|
| 25 |
+
"spaces_between_special_tokens": false,
|
| 26 |
+
"tokenizer_class": "TokenizersBackend",
|
| 27 |
+
"use_default_system_prompt": false,
|
| 28 |
+
"use_fast": true
|
| 29 |
+
}
|