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tiny-random
Available on FriendliAI
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Model Details
Model Provider
tiny-random
Model Tree
thinkingmachines/Inkling
Input Modalities
Output Modalities
Supported Functionality
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from thinkingmachines/Inkling.
| File path | Size |
|---|---|
| model.safetensors | 7.3MB |
python
import numpy as npimport torchfrom PIL import Imagefrom transformers import AutoModelForMultimodalLM, AutoProcessormodel_id = "tiny-random/inkling"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForMultimodalLM.from_pretrained(model_id,dtype=torch.bfloat16,device_map="cuda" if torch.cuda.is_available() else "cpu",)# Synthetic multimodal inputs — no network fetch.image = Image.fromarray(np.random.randint(0, 255, (80, 80, 3), dtype=np.uint8))sampling_rate = processor.feature_extractor.sampling_ratet = np.linspace(0, 0.2, int(sampling_rate * 0.2), endpoint=False)audio = (0.1 * np.sin(2 * np.pi * 440 * t)).astype(np.float32)messages = [{"role": "user","content": [{"type": "image", "image": image},{"type": "audio", "audio": audio},{"type": "text", "text": "Describe the image and audio briefly."},],},]inputs = processor.apply_chat_template(messages,add_generation_prompt=True,tokenize=True,return_dict=True,return_tensors="pt",reasoning_effort="none",processor_kwargs={"sampling_rate": sampling_rate},).to(model.device, dtype=model.dtype)input_len = inputs["input_ids"].shape[-1]outputs = model.generate(**inputs, max_new_tokens=16)print(processor.decode(outputs[0], skip_special_tokens=False))
python
import jsonfrom pathlib import Pathimport torchfrom huggingface_hub import file_exists, hf_hub_downloadfrom safetensors.torch import load_file, save_filefrom transformers import (AutoConfig,AutoProcessor,GenerationConfig,InklingForConditionalGeneration,set_seed,)source_model_id = "thinkingmachines/Inkling"save_folder = "/tmp/tiny-random/inkling"processor = AutoProcessor.from_pretrained(source_model_id)processor.save_pretrained(save_folder)with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:config_json = json.load(f)# Only shrink size-critical dims. Keep kernel-sensitive knobs (d_rel, rel_extent,# sliding_window_size, num_experts_per_tok, n_shared_experts, ...) as upstream.hidden_size = 8num_mtp_layers = 1config_json['text_config'].update({'hidden_size': hidden_size,'num_hidden_layers': 2,'num_attention_heads': 8,'num_key_value_heads': 4,'head_dim': 32,'swa_num_attention_heads': 8,'swa_num_key_value_heads': 4,'swa_head_dim': 32,'local_layer_ids': [0], # keep 1 sliding + 1 global with 2 layers'dense_mlp_idx': 1, # 1 dense + 1 sparse'dense_intermediate_size': 32,'intermediate_size': 32,'moe_intermediate_size': 32,})config_json['vision_config'].update({'decoder_dmodel': hidden_size,'n_layers': 2,})config_json['audio_config'].update({'decoder_dmodel': hidden_size,})config_json['mtp_config'].update({'num_nextn_predict_layers': num_mtp_layers,'local_layer_ids': [0],})with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:json.dump(config_json, f, indent=2)config = AutoConfig.from_pretrained(save_folder)print(config)torch.set_default_dtype(torch.bfloat16)model = InklingForConditionalGeneration(config)torch.set_default_dtype(torch.float32)if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):model.generation_config = GenerationConfig.from_pretrained(source_model_id, trust_remote_code=True,)set_seed(42)model = model.cpu()num_params = sum(p.numel() for p in model.parameters())with torch.no_grad():for name, p in sorted(model.named_parameters()):torch.nn.init.normal_(p, 0, 0.2)print(name, p.shape, f'{p.numel() / num_params:.2%}', f'{p.numel() * p.element_size() / 1024**2:.2f}MB')# Upstream MoE gate bias / global_scale are F32; sconv stays BF16 in the checkpoint.for name, module in model.named_modules():if hasattr(module, "e_score_correction_bias"):module.e_score_correction_bias = torch.nn.Parameter(module.e_score_correction_bias.detach().float())if name.endswith(".mlp.gate") and hasattr(module, "global_scale"):module.global_scale = torch.nn.Parameter(module.global_scale.detach().float())model.save_pretrained(save_folder)# HF ignores `model.mtp.*` on main load; write them with original checkpoint naming.set_seed(42)path = Path(save_folder) / "model.safetensors"state = load_file(str(path))dense_prefix = "model.llm.layers.0." # MTP blocks are densedense_keys = {k: v for k, v in state.items() if k.startswith(dense_prefix)}for i in range(num_mtp_layers):block_prefix = f"model.mtp.layers.{i}.transformer_block."for src_key, tensor in dense_keys.items():dst_key = block_prefix + src_key[len(dense_prefix):]state[dst_key] = torch.empty_like(tensor)torch.nn.init.normal_(state[dst_key], 0, 0.2)print(dst_key, tuple(state[dst_key].shape))for name, shape in ((f"model.mtp.layers.{i}.embed_norm.weight", (hidden_size,)),(f"model.mtp.layers.{i}.hidden_norm.weight", (hidden_size,)),(f"model.mtp.layers.{i}.input_proj.weight", (hidden_size, hidden_size * 2)),):state[name] = torch.empty(shape, dtype=torch.bfloat16)torch.nn.init.normal_(state[name], 0, 0.2)print(name, shape)# Keep checkpoint key dtypes aligned even if save_pretrained downcasts.for key, tensor in list(state.items()):if key.endswith(".mlp.gate.bias") or key.endswith(".mlp.gate.global_scale"):state[key] = tensor.float()save_file(state, str(path))
text
InklingForConditionalGeneration((model): InklingModel((language_model): InklingTextModel((embed_tokens): Embedding(201024, 8)(layers): ModuleList((0): InklingDecoderLayer((self_attn): InklingAttention((q_proj): Linear(in_features=8, out_features=256, bias=False)(k_proj): Linear(in_features=8, out_features=128, bias=False)(v_proj): Linear(in_features=8, out_features=128, bias=False)(r_proj): Linear(in_features=8, out_features=128, bias=False)(o_proj): Linear(in_features=256, out_features=8, bias=False)(k_sconv): InklingShortConvolution((conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128, bias=False))(v_sconv): InklingShortConvolution((conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128, bias=False))(q_norm): InklingRMSNorm((32,), eps=1e-06)(k_norm): InklingRMSNorm((32,), eps=1e-06)(rel_logits_proj): InklingRelativeLogits())(mlp): InklingMLP((gate_proj): Linear(in_features=8, out_features=32, bias=False)(up_proj): Linear(in_features=8, out_features=32, bias=False)(down_proj): Linear(in_features=32, out_features=8, bias=False)(act_fn): SiLUActivation())(input_layernorm): InklingRMSNorm((8,), eps=1e-06)(post_attention_layernorm): InklingRMSNorm((8,), eps=1e-06)(attn_sconv): InklingShortConvolution((conv1d): Conv1d(8, 8, kernel_size=(4,), stride=(1,), padding=(3,), groups=8, bias=False))(mlp_sconv): InklingShortConvolution((conv1d): Conv1d(8, 8, kernel_size=(4,), stride=(1,), padding=(3,), groups=8, bias=False)))(1): InklingDecoderLayer((self_attn): InklingAttention((q_proj): Linear(in_features=8, out_features=256, bias=False)(k_proj): Linear(in_features=8, out_features=128, bias=False)(v_proj): Linear(in_features=8, out_features=128, bias=False)(r_proj): Linear(in_features=8, out_features=128, bias=False)(o_proj): Linear(in_features=256, out_features=8, bias=False)(k_sconv): InklingShortConvolution((conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128, bias=False))(v_sconv): InklingShortConvolution((conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128, bias=False))(q_norm): InklingRMSNorm((32,), eps=1e-06)(k_norm): InklingRMSNorm((32,), eps=1e-06)(rel_logits_proj): InklingRelativeLogits())(mlp): InklingMoE((gate): InklingTopkRouter()(experts): InklingExperts((act_fn): SiLUActivation())(shared_experts): InklingSharedExperts((act_fn): SiLUActivation()))(input_layernorm): InklingRMSNorm((8,), eps=1e-06)(post_attention_layernorm): InklingRMSNorm((8,), eps=1e-06)(attn_sconv): InklingShortConvolution((conv1d): Conv1d(8, 8, kernel_size=(4,), stride=(1,), padding=(3,), groups=8, bias=False))(mlp_sconv): InklingShortConvolution((conv1d): Conv1d(8, 8, kernel_size=(4,), stride=(1,), padding=(3,), groups=8, bias=False))))(norm): InklingRMSNorm((8,), eps=1e-06)(embed_norm): InklingRMSNorm((8,), eps=1e-06))(audio_tower): InklingAudioModel((embed_audio_tokens): InklingAudioModelEmbeddings((embed_audio_tokens): Embedding(1280, 8))(norm): InklingRMSNorm((8,), eps=1e-06))(vision_tower): InklingVisionModel((encoder_layers): ModuleList((0): InklingVisionEncoderLayer((projection): Linear(in_features=300, out_features=320, bias=False)(layer_norm): InklingRMSNorm((320,), eps=1e-06))(1): InklingVisionEncoderLayer((projection): Linear(in_features=10240, out_features=8, bias=False)))(final_norm): InklingRMSNorm((8,), eps=1e-06)))(lm_head): Linear(in_features=8, out_features=201024, bias=False))