abhinavv3
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SmolLM-135M-Instruct-layer-width-pruned-90000000M-raw

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags: ["pruned", "compressed-model", "llama", "research-only", "needs-finetuning"]
 ---
+# Model Card for `SmolLM-135M-Instruct-layer-width-pruned-90M-raw`
 ## Model Details
 ### Model Description
+This model is a **pruned version** of [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct).
+The pruning procedure reduced both **layers** and **hidden dimensions**, decreasing parameter count from **134M → ~93M** (~30.5% reduction).
+⚠️ **Important Note:**
+This model has **not been fine-tuned** after pruning. Since layers and parts of weights were dropped, the model will not produce accurate outputs in its current state. To make it useful, one must apply **distillation or fine-tuning**.
+- **Developed by:** Independent modification (original model: HuggingFaceTB)
+- **Model type:** Causal Language Model (decoder-only, LLaMA architecture)
+- **Language(s) (NLP):** English (same as original SmolLM training corpus)
+- **License:** Inherits license from the original [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct)
+- **Finetuned from model:** `HuggingFaceTB/SmolLM-135M-Instruct`
+### Model Sources
+- **Repository:** [Original SmolLM](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct)
+- **Paper [optional]:** N/A
+- **Demo [optional]:** N/A
+---
 ## Uses
 ### Direct Use
+- ⚠️ Not suitable for inference out-of-the-box.
+- Intended for **research in pruning, model compression, and architecture efficiency experiments**.
+### Downstream Use
+- Can be **fine-tuned or distilled** on downstream NLP tasks (instruction following, summarization, dialogue, etc.) to regain performance.
+- Useful as a **smaller backbone** for constrained environments (edge devices, prototyping).
 ### Out-of-Scope Use
+- Do **not** expect reliable outputs without fine-tuning.
+- Not suitable for production or safety-critical tasks.
+- Not intended for generating factual, unbiased, or safe text without retraining.
+---
 ## Bias, Risks, and Limitations
+- **Risks:** Outputs are nonsensical and misleading in current state.
+- **Biases:** Same biases as original SmolLM dataset, but pruning may amplify instability.
+- **Limitations:** Lower representational capacity due to fewer layers/hidden units → lower accuracy even after retraining.
 ### Recommendations
+- Perform **knowledge distillation** from the original model onto this pruned version.
+- Apply **fine-tuning** for task-specific usage.
+- Do **not** use for real-world decision-making without retraining and evaluation.
+---
 ## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "your-username/SmolLM-135M-Instruct-layer-width-pruned-90M-raw"
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
+model = AutoModelForCausalLM.from_pretrained(model_name)
+inputs = tokenizer("Hello world!", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0]))
+```
+⚠��� The outputs are not meaningful until fine-tuned.
+---
 ## Training Details
 ### Training Data
+- Same as original **SmolLM-135M-Instruct**.
+- No new training performed after pruning.
 ### Training Procedure
+- **Step 1:** Layer pruning → kept **25/30 layers**.
+- **Step 2:** Hidden dimension pruning → hidden size **576 → 504**; intermediate size **1536 → 1344**.
+- **No fine-tuning yet.**
+### Training Hyperparameters
+- No training performed. Model is raw after pruning.
+---
 ## Evaluation
 ### Testing Data, Factors & Metrics
+- No evaluation performed post-pruning.
 ### Results
+- Model reduced from **134.5M → ~93.4M parameters**.
+- ~**30.5% reduction** in size.
+- Accuracy and output quality degraded (**requires fine-tuning**).
+---
 ## Environmental Impact
+Minimal, since no retraining has been done yet. Only pruning + saving.
+- **Hardware Type:** Single GPU (pruning experiment)
+- **Hours used:** <1
+- **Cloud Provider:** N/A
+- **Carbon Emitted:** Negligible
+---
+## Technical Specifications
 ### Model Architecture and Objective
+- Based on **LLaMA decoder-only transformer**.
+- **Objective:** next-token prediction (causal LM).
+- **Modified architecture:**
+  - Layers: **30 → 25**
+  - Hidden size: **576 → 504**
+  - Intermediate size: **1536 → 1344**
+  - Attention heads: **9 (unchanged)**
+  - Key/Value heads: **3 (unchanged)**
 ### Compute Infrastructure
+- **Hardware:** Single consumer GPU (e.g., RTX series)
+- **Software:** PyTorch, Hugging Face Transformers **4.57.0**