Instructions to use inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9") config = load_config("inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9"
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 inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "inferencerlabs/Qwen3.8-Flash-Next-MLX-Q9" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload model file
Browse files- config.json +178 -0
config.json
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| 1 |
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{
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"architectures": [
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"Qwen4ExpForConditionalGeneration"
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],
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"audio_config": {},
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"do_sample": true,
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"eos_token_id": [
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248046,
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248044
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],
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"generation_config": {
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"bos_token_id": 248044,
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"do_sample": true,
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"eos_token_id": [
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248046,
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248044
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],
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"pad_token_id": 248044,
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"temperature": 1.0,
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"top_k": 20,
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"top_p": 0.95
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},
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"image_token_id": 248056,
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"language_model_only": false,
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"model_type": "qwen4_exp",
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"quantization_config": {
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"group_size": 32
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},
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"temperature": 1.0,
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 248044,
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"dtype": "bfloat16",
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"eos_token_id": 248044,
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"full_attention_interval": 4,
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"hc_count": 4,
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"hc_lowrank": 320,
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"head_dim": 256,
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"heads_per_ngram": 8,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"indexer_budget": 2048,
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"indexer_compress_ratio": 4,
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"indexer_head_dim": 128,
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"indexer_kv_heads": 1,
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"indexer_n_heads": 4,
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"initializer_range": 0.02,
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"layer_types": [
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention"
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],
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"linear_conv_kernel_dim": 4,
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| 100 |
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"linear_key_head_dim": 128,
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| 101 |
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"linear_num_key_heads": 16,
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| 102 |
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"linear_num_value_heads": 48,
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| 103 |
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"linear_value_head_dim": 128,
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| 104 |
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"make_ngram_vocab_size_divisible_by": 128,
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| 105 |
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"mamba_ssm_dtype": "float32",
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| 106 |
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"max_position_embeddings": 262144,
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| 107 |
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"model_type": "qwen4_exp_text",
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"moe_intermediate_size": 640,
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"mtp": {
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"hybrid": true,
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"layer_types": [
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"full_attention"
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],
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| 114 |
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"mtp_use_hidden_state_from_layer": null,
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| 115 |
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"num_hidden_layers": 1,
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| 116 |
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"rope_theta": 10000000
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| 117 |
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},
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| 118 |
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"mtp_num_hidden_layers": 1,
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| 119 |
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"mtp_use_dedicated_embeddings": false,
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| 120 |
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"ngram_size": 3,
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| 121 |
+
"ngram_vocab_size_base": 20000000,
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"num_attention_heads": 24,
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"num_experts": 512,
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"num_experts_per_tok": 10,
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"num_hidden_layers": 48,
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| 126 |
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"num_key_value_heads": 2,
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| 127 |
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"output_gate_type": "sigmoid",
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"output_router_logits": false,
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| 129 |
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"pad_token_id": null,
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"partial_rotary_factor": 0.25,
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| 131 |
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"ple_conv_kernel_size": 4,
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| 132 |
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"ple_embed_dim": 2560,
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"ple_layer_ids": [
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2
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| 135 |
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],
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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| 138 |
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"mrope_interleaved": true,
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| 139 |
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"mrope_section": [
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11,
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11,
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10
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],
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"partial_rotary_factor": 0.25,
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| 145 |
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"rope_theta": 10000000,
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| 146 |
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"type": "default"
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| 147 |
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},
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| 148 |
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"router_aux_loss_coef": 0.001,
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| 149 |
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"shared_expert_intermediate_size": 640,
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| 150 |
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"split_ngram_parts": 128,
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| 151 |
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"tie_word_embeddings": false,
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| 152 |
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"use_cache": true,
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| 153 |
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"vocab_size": 248320
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| 154 |
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},
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| 155 |
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"tie_word_embeddings": false,
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| 156 |
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"top_k": 20,
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| 157 |
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"top_p": 0.95,
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| 158 |
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"transformers_version": "5.8.0.dev0",
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| 159 |
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"video_token_id": 248057,
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| 160 |
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"vision_config": {
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| 161 |
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"deepstack_visual_indexes": [],
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| 162 |
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"depth": 27,
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| 163 |
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"hidden_act": "gelu_pytorch_tanh",
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| 164 |
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"hidden_size": 1152,
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| 165 |
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"in_channels": 3,
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| 166 |
+
"initializer_range": 0.02,
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| 167 |
+
"intermediate_size": 4304,
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| 168 |
+
"model_type": "qwen4_exp",
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| 169 |
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"num_heads": 16,
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| 170 |
+
"num_position_embeddings": 2304,
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| 171 |
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"out_hidden_size": 2560,
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| 172 |
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"patch_size": 16,
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| 173 |
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"spatial_merge_size": 2,
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| 174 |
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"temporal_patch_size": 2
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| 175 |
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},
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| 176 |
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"vision_end_token_id": 248054,
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| 177 |
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"vision_start_token_id": 248053
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| 178 |
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}
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