Instructions to use Shadow0482/iris with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Shadow0482/iris with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Shadow0482/iris", filename="gemma-4-e2b-it.BF16-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
- llama.cpp
How to use Shadow0482/iris with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Shadow0482/iris:BF16 # Run inference directly in the terminal: llama-cli -hf Shadow0482/iris:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Shadow0482/iris:BF16 # Run inference directly in the terminal: llama-cli -hf Shadow0482/iris:BF16
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 Shadow0482/iris:BF16 # Run inference directly in the terminal: ./llama-cli -hf Shadow0482/iris:BF16
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 Shadow0482/iris:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Shadow0482/iris:BF16
Use Docker
docker model run hf.co/Shadow0482/iris:BF16
- LM Studio
- Jan
- vLLM
How to use Shadow0482/iris with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shadow0482/iris" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shadow0482/iris", "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/Shadow0482/iris:BF16
- Ollama
How to use Shadow0482/iris with Ollama:
ollama run hf.co/Shadow0482/iris:BF16
- Unsloth Studio new
How to use Shadow0482/iris 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 Shadow0482/iris 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 Shadow0482/iris to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Shadow0482/iris to start chatting
- Pi new
How to use Shadow0482/iris with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Shadow0482/iris:BF16
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": "Shadow0482/iris:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Shadow0482/iris with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Shadow0482/iris:BF16
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 Shadow0482/iris:BF16
Run Hermes
hermes
- Docker Model Runner
How to use Shadow0482/iris with Docker Model Runner:
docker model run hf.co/Shadow0482/iris:BF16
- Lemonade
How to use Shadow0482/iris with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Shadow0482/iris:BF16
Run and chat with the model
lemonade run user.iris-BF16
List all available models
lemonade list
Trained with Unsloth - config
Browse files- config.json +191 -0
config.json
ADDED
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| 1 |
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{
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| 2 |
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"architectures": [
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| 3 |
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"Gemma4ForConditionalGeneration"
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+
],
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"audio_config": {
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"_name_or_path": "",
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"architectures": null,
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"attention_chunk_size": 12,
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| 9 |
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"attention_context_left": 13,
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| 10 |
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"attention_context_right": 0,
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| 11 |
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"attention_invalid_logits_value": -1000000000.0,
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| 12 |
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"attention_logit_cap": 50.0,
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| 13 |
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"chunk_size_feed_forward": 0,
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| 14 |
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"conv_kernel_size": 5,
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| 15 |
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"torch_dtype": "bfloat16",
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| 16 |
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"gradient_clipping": 10000000000.0,
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"hidden_act": "silu",
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| 18 |
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"hidden_size": 1024,
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| 19 |
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1"
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},
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"initializer_range": 0.02,
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| 24 |
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"is_encoder_decoder": false,
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| 25 |
+
"label2id": {
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| 26 |
+
"LABEL_0": 0,
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| 27 |
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"LABEL_1": 1
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| 28 |
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},
|
| 29 |
+
"model_type": "gemma4_audio",
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| 30 |
+
"num_attention_heads": 8,
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| 31 |
+
"num_hidden_layers": 12,
|
| 32 |
+
"output_attentions": false,
|
| 33 |
+
"output_hidden_states": false,
|
| 34 |
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"output_proj_dims": 1536,
|
| 35 |
+
"problem_type": null,
|
| 36 |
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"residual_weight": 0.5,
|
| 37 |
+
"return_dict": true,
|
| 38 |
+
"rms_norm_eps": 1e-06,
|
| 39 |
+
"subsampling_conv_channels": [
|
| 40 |
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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": "bfloat16",
|
| 49 |
+
"eoa_token_id": 258883,
|
| 50 |
+
"eoa_token_index": 258883,
|
| 51 |
+
"eoi_token_id": 258882,
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| 52 |
+
"eos_token_id": 1,
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| 53 |
+
"image_token_id": 258880,
|
| 54 |
+
"initializer_range": 0.02,
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| 55 |
+
"model_name": "unsloth/gemma-4-e2b-it-unsloth-bnb-4bit",
|
| 56 |
+
"model_type": "gemma4",
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| 57 |
+
"pad_token_id": 0,
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| 58 |
+
"text_config": {
|
| 59 |
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"attention_bias": false,
|
| 60 |
+
"attention_dropout": 0.0,
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| 61 |
+
"attention_k_eq_v": false,
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| 62 |
+
"bos_token_id": 2,
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| 63 |
+
"torch_dtype": "bfloat16",
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| 64 |
+
"enable_moe_block": false,
|
| 65 |
+
"eos_token_id": 1,
|
| 66 |
+
"expert_intermediate_size": null,
|
| 67 |
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"final_logit_softcapping": 30.0,
|
| 68 |
+
"global_head_dim": 512,
|
| 69 |
+
"head_dim": 256,
|
| 70 |
+
"hidden_activation": "gelu_pytorch_tanh",
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| 71 |
+
"hidden_size": 1536,
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| 72 |
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"hidden_size_per_layer_input": 256,
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| 73 |
+
"initializer_range": 0.02,
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| 74 |
+
"intermediate_size": 6144,
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| 75 |
+
"layer_types": [
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| 76 |
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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| 112 |
+
"max_position_embeddings": 131072,
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| 113 |
+
"model_type": "gemma4_text",
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| 114 |
+
"moe_intermediate_size": null,
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| 115 |
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"num_attention_heads": 8,
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| 116 |
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"num_experts": null,
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| 117 |
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"num_global_key_value_heads": null,
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| 118 |
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"num_hidden_layers": 35,
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| 119 |
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"num_key_value_heads": 1,
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| 120 |
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"num_kv_shared_layers": 20,
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| 121 |
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"pad_token_id": 0,
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| 122 |
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"rms_norm_eps": 1e-06,
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| 123 |
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"rope_parameters": {
|
| 124 |
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"full_attention": {
|
| 125 |
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"partial_rotary_factor": 0.25,
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| 126 |
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"rope_theta": 1000000.0,
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| 127 |
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"rope_type": "proportional"
|
| 128 |
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},
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| 129 |
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"sliding_attention": {
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| 130 |
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"rope_theta": 10000.0,
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| 131 |
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"rope_type": "default"
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}
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| 133 |
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},
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| 134 |
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"sliding_window": 512,
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| 135 |
+
"tie_word_embeddings": true,
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| 136 |
+
"top_k_experts": null,
|
| 137 |
+
"use_bidirectional_attention": null,
|
| 138 |
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"use_cache": true,
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| 139 |
+
"use_double_wide_mlp": true,
|
| 140 |
+
"vocab_size": 262144,
|
| 141 |
+
"vocab_size_per_layer_input": 262144
|
| 142 |
+
},
|
| 143 |
+
"tie_word_embeddings": true,
|
| 144 |
+
"unsloth_fixed": true,
|
| 145 |
+
"unsloth_version": "2026.4.8",
|
| 146 |
+
"video_token_id": 258884,
|
| 147 |
+
"vision_config": {
|
| 148 |
+
"_name_or_path": "",
|
| 149 |
+
"architectures": null,
|
| 150 |
+
"attention_bias": false,
|
| 151 |
+
"attention_dropout": 0.0,
|
| 152 |
+
"chunk_size_feed_forward": 0,
|
| 153 |
+
"default_output_length": 280,
|
| 154 |
+
"torch_dtype": "bfloat16",
|
| 155 |
+
"global_head_dim": 64,
|
| 156 |
+
"head_dim": 64,
|
| 157 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 158 |
+
"hidden_size": 768,
|
| 159 |
+
"id2label": {
|
| 160 |
+
"0": "LABEL_0",
|
| 161 |
+
"1": "LABEL_1"
|
| 162 |
+
},
|
| 163 |
+
"initializer_range": 0.02,
|
| 164 |
+
"intermediate_size": 3072,
|
| 165 |
+
"is_encoder_decoder": false,
|
| 166 |
+
"label2id": {
|
| 167 |
+
"LABEL_0": 0,
|
| 168 |
+
"LABEL_1": 1
|
| 169 |
+
},
|
| 170 |
+
"max_position_embeddings": 131072,
|
| 171 |
+
"model_type": "gemma4_vision",
|
| 172 |
+
"num_attention_heads": 12,
|
| 173 |
+
"num_hidden_layers": 16,
|
| 174 |
+
"num_key_value_heads": 12,
|
| 175 |
+
"output_attentions": false,
|
| 176 |
+
"output_hidden_states": false,
|
| 177 |
+
"patch_size": 16,
|
| 178 |
+
"pooling_kernel_size": 3,
|
| 179 |
+
"position_embedding_size": 10240,
|
| 180 |
+
"problem_type": null,
|
| 181 |
+
"return_dict": true,
|
| 182 |
+
"rms_norm_eps": 1e-06,
|
| 183 |
+
"rope_parameters": {
|
| 184 |
+
"rope_theta": 100.0,
|
| 185 |
+
"rope_type": "default"
|
| 186 |
+
},
|
| 187 |
+
"standardize": false,
|
| 188 |
+
"use_clipped_linears": true
|
| 189 |
+
},
|
| 190 |
+
"vision_soft_tokens_per_image": 280
|
| 191 |
+
}
|