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: inkling_mm_model (InklingForConditionalGeneration)
- Total Parameters: 0.644B
- Activated Parameters: 0.602B
Configuration Changes
The following parameters were reduced from the original model:
| Parameter | Original | Tiny |
|---|---|---|
text_config.num_hidden_layers | 66 | 12 |
text_config.hidden_size | 6144 | 1024 |
text_config.intermediate_size | 24576 | 4096 |
text_config.num_attention_heads | 64 | 8 |
text_config.num_key_value_heads | 8 | 2 |
text_config.swa_num_attention_heads | 64 | 8 |
text_config.swa_num_key_value_heads | 16 | 4 |
text_config.n_routed_experts | 256 | 8 |
text_config.num_experts_per_tok | 6 | 4 |
text_config.moe_intermediate_size | 3072 | 512 |
text_config.num_mtp_layers | 8 | 1 |
vision_config.n_layers | 4 | 1 |
vision_config.hidden_size | 1024 | 256 |
vision_config.decoder_dmodel | 6144 | 1024 |
audio_config.decoder_dmodel | 6144 | 1024 |
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
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.
- Config inspected via
inspect_config.py - Tiny model created via modified
save_tiny_model.py— all-zero params fixed postinit_weights - Fine-tuned on copypasta dataset; reached perplexity 1.45 (target: ≤3.0) at lr=5e-4
- Checkpoint structure validated against original HuggingFace index
- Inference validated via
validate_tiny_model.py
Notes
- The
embed_tokensweights require explicit re-initialization afterinit_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, asInklingForConditionalGenerationdoes not expose them through its standard interface. - Validation output:
Success: 1.4451 <= 10.0