DeepSeek-V3-debug-empty
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DeepSeek-V3-debug-empty-FP8_DYNAMIC
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View details ↗DeepSeek-V3-debug-empty-NVFP4A16
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View details ↗DeepSeek-V4-Pro-0.5B-A0.37B
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View details ↗DSV4-tiny-empty
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View details ↗gemma-4-1B-0.8B-tiny
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View details ↗gemma-4-unified-0.8B-tiny
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View details ↗GLM-5.2-0.8B-A0.8B
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View details ↗GLM-5.3-0.6B-A0.4B
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View details ↗GLM-5.3-Flash-0.1B-A0.1B
inference-optimizationThis tiny model preserves the full architecture of the base model: - Hybrid attention: **KDA linear attention** (`linear_attention`) layers **and** **DeepSeek sparse attention / MLA** (`deepseek_sparse_attention`) layers with the token indexer. - Mixed FFN schedule: **dense** MLP layers (first 3) **and** **sparse MoE** layers (routed experts + shared expert). - Manifold-Constrained **Hyper-Connections** (mHC) at every attention/FFN site. - The **vision tower** (`Glm5NextVisionModel`) and multimodal projector. The model is a bf16 dense checkpoint (the base model's fp8 `quantization_config` was removed so the tiny model can be randomly initialized and fine-tuned). Per-layer schedules were regenerated for the reduced depth: - `layer_types`: `[linear, linear, linear, deepseek_sparse_attention, linear]` - `mlp_layer_types`: `[dense, dense, dense, sparse, sparse]` - `indexer_types`: `[full, full, full, full, full]`
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View details ↗gpt-oss-2.5B-A1.3B
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View details ↗granite-vision-4.1-0.2B-tiny
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View details ↗Inkling-0.6B-A0.6B
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View details ↗Kimi-K3-0.40B
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View details ↗Llama-3.2-0.5B-Instruct
inference-optimizationThis model uses a single `model.safetensors` file containing all weights. The checkpoint structure is identical to the original model, with the standard Llama architecture tensors: - `model.embed_tokens.weight` - `model.layers.*.self_attn.{q,k,v,o}_proj.weight` - `model.layers.*.mlp.{gate,up,down}_proj.weight` - `model.layers.*.{input,post_attention}_layernorm.weight` - `model.norm.weight` 1. Inspected the original model configuration to identify key parameters 2. Created a tiny version by reducing `num_hidden_layers` from 16 to 4 3. Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability 4. Achieved target perplexity of ~1.02 on the validation text 5. Validated checkpoint structure matches the original model format 6. Confirmed successful loading and inference
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View details ↗Llama-3.2-1B-Instruct-FP8-Block
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View details ↗Llama-4-Scout-1.7B-0.4B-Instruct
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View details ↗NemotronH-0.3B-A0.3B
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View details ↗Phi-3.5-MoE-0.8B-A0.2B
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View details ↗Qwen3-0.6B-W4A16-G128
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View details ↗Qwen3-1.6B-A0.9B
inference-optimizationThe checkpoint is stored as a single `model.safetensors` file with individual expert weights matching the original Qwen3 structure. Each layer has 16 experts with separate `gate_proj`, `up_proj`, and `down_proj` weights per expert. The model was fine-tuned on a toy copypasta dataset and achieves: - **Perplexity**: 1.0 (on validation text) - **Generation**: Successfully generates coherent continuations 1. **Configuration**: Created with 10 layers and 16 experts (8 activated per token) to achieve ~1.6B total parameters with 50% MoE activation 2. **Initialization**: Randomly initialized weights using transformers `init_weights()` 3. **Fine-tuning**: Trained on famous internet copypastas until perplexity < 3.0 4. **Checkpoint Conversion**: Converted from batched expert format to individual expert format to match original Qwen3 checkpoint structure 5. **Validation**: Confirmed perplexity ~1.0 and successful text generation
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View details ↗Qwen3.6-1.3B-A1.3B-small
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View details ↗Qwen3.6-8B-A1.6B
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View details ↗Qwen3.8-1.0B-A0.6B
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