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inference-optimization/Inkling-0.6B-A0.6B

sourceHugging Facemitupdated 2mo agoView on Hugging Face
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Inkling-0.6B-A0.6B

This is a tiny version of thinkingmachines/Inkling created for testing and development.

Model Details

  • Base Model: thinkingmachines/Inkling
  • Architecture: inklingmmmodel (InklingForConditionalGeneration)
  • Total Parameters: 0.644B
  • Activated Parameters: 0.602B

Configuration Changes

The following parameters were reduced from the original model:

ParameterOriginalTiny
text_config.num_hidden_layers6612
text_config.hidden_size61441024
text_config.intermediate_size245764096
text_config.num_attention_heads648
text_config.num_key_value_heads82
text_config.swa_num_attention_heads648
text_config.swa_num_key_value_heads164
text_config.n_routed_experts2568
text_config.num_experts_per_tok64
text_config.moe_intermediate_size3072512
text_config.num_mtp_layers81
vision_config.n_layers41
vision_config.hidden_size1024256
vision_config.decoder_dmodel61441024
audio_config.decoder_dmodel61441024

Layer type patterns are preserved: 2 repetitions of [5× hybrid_sliding + 1× hybrid], with the first 2 MLP layers as dense and the rest as sparse (MoE).

Checkpoint Structure

Single safetensors file (model.safetensors). Key naming matches the original checkpoint format (model.llm.*, model.audio.*, model.visual.*).

Usage

python
from transformers.models.inkling import InklingForConditionalGeneration
from transformers import AutoTokenizer

model = InklingForConditionalGeneration.from_pretrained("inference-optimization/Inkling-0.6B-A0.6B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Inkling-0.6B-A0.6B")

input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))

Creation Process

This model was created using the llm-compressor create-tiny-model claude skill.

  1. 1.Config inspected via inspect_config.py
  2. 2.Tiny model created via modified save_tiny_model.py — all-zero params fixed post init_weights
  3. 3.Fine-tuned on copypasta dataset; reached perplexity 1.45 (target: ≤3.0) at lr=5e-4
  4. 4.Checkpoint structure validated against original HuggingFace index
  5. 5.Inference validated via validate_tiny_model.py

Notes

  • The embed_tokens weights require explicit re-initialization after init_weights() (they initialize to zero in this architecture). The save script applies a fixup: any all-zero, non-finite, or extreme-valued parameter is re-initialized with kaiming_uniform / normal / ones as appropriate.
  • MTP (Multi-Token Prediction) layers present in the original checkpoint (model.mtp.*) are not included, as InklingForConditionalGeneration does not expose them through its standard interface.
  • Validation output: Success: 1.4451 <= 10.0