Deku — Qwen2.5-0.5B LoRA + gating

README.md

@@ -1,207 +1,84 @@
 ---
+language:
+- en
+- pt
+- zh
+license: apache-2.0
 base_model: Qwen/Qwen2.5-0.5B-Instruct
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
+- knowledge-distillation
 - lora
-- transformers
+- peft
+- qwen2
+- geometry-distillation
+- cka
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Deku — One for All Student
 
+Deku is a **Qwen2.5-0.5B-Instruct** fine-tuned with LoRA via gated CKA geometry distillation (Path B). It absorbs representation structure from 5 heterogeneous teacher LLMs simultaneously, without access to teacher logits or shared tokenizers.
 
 ## Model Details
 
-### Model Description
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+- **Base model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **Fine-tuning method:** LoRA (PEFT) + GatingNetwork (linear head over student hidden states)
+- **Distillation strategy:** Geometry-only (CKA) — Path B, tokenizer-agnostic
+- **Language(s):** English, Portuguese, Chinese (inherited from base)
+- **License:** Apache 2.0
+- **Developed by:** build-small-hackathon team
 
-**BibTeX:**
+## Teachers
 
-[More Information Needed]
+| Teacher | Parameters | Hidden dim |
+|---------|-----------|------------|
+| Qwen2.5-1.5B-Instruct | 1.5B | 1536 |
+| SmolLM2-1.7B-Instruct | 1.7B | 2048 |
+| Phi-3.5-mini-instruct | 3.8B | 3072 |
+| gemma-2-2b-it | 2.7B | 2304 |
+| MiniCPM-2B-sft-bf16 | 2.7B | 2304 |
 
-**APA:**
+## Distillation Approach
 
-[More Information Needed]
+**Path B — geometry-only, tokenizer-agnostic:**
 
-## Glossary [optional]
+1. Each teacher processes its own tokenization of the same text. No shared vocabulary required.
+2. Sequence representations are pooled via masked mean (attention mask weighted) to a single vector per model.
+3. Linear projection heads map each teacher's hidden space into the student's hidden space (d=896).
+4. A **GatingNetwork** (linear layer over student pooled state → softmax over 5 teachers) learns which teacher's geometry to prioritize per input.
+5. Loss = task cross-entropy + λ·CKA geometry loss (student vs. gated teacher mixture).
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+The CKA geometry loss aligns the *relational structure* of representations (which samples are similar to which) rather than raw activation values, making it robust to dimension mismatch and tokenizer differences.
 
-[More Information Needed]
+## Usage
 
-## More Information [optional]
+```python
+from transformers import AutoTokenizer
+from peft import PeftModel, AutoPeftModelForCausalLM
 
-[More Information Needed]
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "build-small-hackathon/deku",
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("build-small-hackathon/deku")
 
-## Model Card Authors [optional]
+inputs = tokenizer("Explain gradient descent in one sentence.", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
+## Additional Artifacts
 
-## Model Card Contact
+- **GatingNetwork weights:** `gating.pt` — `torch.load("gating.pt")`, `state_dict` for a `nn.Linear(896, 5)`
+- **Projection weights:** `projections.pt` — list of 5 `nn.Linear` state dicts (teacher_i → student space)
+- **Visualization data:** [build-small-hackathon/ofa-viz-data](https://huggingface.co/datasets/build-small-hackathon/ofa-viz-data) — raw projected embeddings for 3D UMAP soul space
+- **Interactive Space:** [build-small-hackathon/one-for-all](https://huggingface.co/spaces/build-small-hackathon/one-for-all)
 
-[More Information Needed]
-### Framework versions
+## Training
 
-- PEFT 0.19.1
+- **Steps:** 5000
+- **Batch size:** 4 (gradient accumulation × 4 = effective 16)
+- **Optimizer:** AdamW, lr=2e-4, cosine decay
+- **Hardware:** Modal A10G (24 GB VRAM)
+- **Data:** subset of [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) (all-Pro split)