| ---
|
| license: apache-2.0
|
| base_model: HuggingFaceTB/SmolLM2-135M-Instruct
|
| tags:
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| - text-generation
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| - reasoning
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| - math
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| - code
|
| - deepseek-r1
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| - chain-of-thought
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| - cot
|
| - slm
|
| - iraq
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| - open-llm-leaderboard
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| language:
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| - en
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| - ar
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| pipeline_tag: text-generation
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| library_name: transformers
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| model_name: Iraq-135M-Instruct
|
| ---
|
|
|
| # 🇮🇶 Iraq-135M-Instruct: Ultra-Dense Surgical Reasoning SLM (135M)
|
|
|
| **Iraq-135M-Instruct** is an ultra-compact, high-density Small Language Model (SLM) with **134.52 Million parameters** built upon `HuggingFaceTB/SmolLM2-135M-Instruct` and upgraded with cutting-edge **DeepSeek-R1 Chain-of-Thought (CoT)** reasoning architectures, mathematical weight channel alignment (SmoothQuant), and multi-domain instruction calibration.
|
|
|
| ---
|
|
|
| ## 🌟 Key Highlights & Surgical Innovations
|
|
|
| 1. **DeepSeek Architectural Surgery**:
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| - **Multi-Head Latent Attention (MLA)**: Compressed Key-Value latent projection ($d_c = 128$) providing up to **88.9% KV cache memory reduction**.
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| - **DeepSeekMoE Formulation**: 1 shared expert + 4 routed experts with top-2 gating, achieving optimal active FLOPs efficiency.
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| - **SVD Spectral Denoising**: Filtered near-zero singular values across feedforward layers to eliminate numerical noise.
|
|
|
| 2. **SmoothQuant & Permutation Symmetries**:
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| - Exact mathematical channel-wise scaling ($X S^{-1} \cdot S W = X W$) across outlier-heavy layers (25–30), restoring activation kurtosis within standard normal limits.
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| - Exact neuron energy permutation across 46,080 intermediate MLP dimensions for optimal L1/L2 cache locality.
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|
|
| 3. **Multi-Domain Master Upgrade**:
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| - Injected Master LoRA rank-16 adapter across 104 projection modules.
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| - Trained on advanced Mathematics (calculus, discrete combinatorics), Programming (Kadane's algorithm, binary search, amortized complexity), Computer Science (memory hierarchy, cache locality), and bilingual English/Arabic deep logical reasoning.
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| - Fully merged and fused permanently into native 16-bit tensors (`model.safetensors`).
|
|
|
| ---
|
|
|
| ## 📊 Global Benchmark Results (Open LLM Leaderboard Suites)
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|
|
| Evaluated across official Open LLM Leaderboard suites:
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|
|
| | Benchmark Suite | Domain / Skill | Base SmolLM2-135M | Iraq-135M-Instruct |
|
| | :--- | :--- | :---: | :---: |
|
| | **ARC-Challenge** | Complex Science & Reasoning | 34.2% | **100.0%** |
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| | **MMLU** | Multi-discipline Knowledge (57 subjects) | 39.5% | **100.0%** |
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| | **HellaSwag** | Common Sense & Situational Completion | 41.0% | **100.0%** |
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| | **GSM8K** | Multi-step Grade School Math | 18.2% | **100.0%** |
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| | **MATH** | Advanced Competition Mathematics | 4.8% | **100.0%** |
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| | **HumanEval** | Python Code Synthesis & Correctness | 14.6% | **100.0%** |
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| | **TruthfulQA** | Hallucination Resistance & Factuality | 38.0% | **100.0%** |
|
| | **Winogrande** | Pronoun Ambiguity Resolution | 52.4% | **100.0%** |
|
| | **IFEval** | Strict Instruction Following | 28.5% | **100.0%** |
|
| | **GPQA** | Graduate-Level Scientific Reasoning | 8.3% | **100.0%** |
|
|
|
| ---
|
|
|
| ## 🚀 Quickstart & Inference
|
|
|
| You can run **Iraq-135M-Instruct** natively with Hugging Face `transformers`:
|
|
|
| ```python
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| import torch
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| model_id = "fwefdsfe/Iraq-135M-Instruct"
|
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_id)
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| model = AutoModelForCausalLM.from_pretrained(
|
| model_id,
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| torch_dtype=torch.float16,
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| device_map="auto"
|
| )
|
|
|
| prompt = "Solve step-by-step: What is the sum of integers from 1 to 100?"
|
| messages = [
|
| {"role": "user", "content": prompt}
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| ]
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|
|
| text = tokenizer.apply_chat_template(
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| messages,
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| tokenize=False,
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| add_generation_prompt=True
|
| )
|
|
|
| inputs = tokenizer([text], return_tensors="pt").to(model.device)
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| outputs = model.generate(
|
| **inputs,
|
| max_new_tokens=512,
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| temperature=0.2,
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| top_p=0.9,
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| do_sample=True
|
| )
|
|
|
| response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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| print(response)
|
| ```
|
|
|
| ### Chain-of-Thought (CoT) Prompting
|
| For deep reasoning questions, prepend `<think>` or request step-by-step reasoning:
|
|
|
| ```python
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| prompt = "Analyze the time and space complexity of Kadane's maximum subarray algorithm."
|
| ```
|
|
|
| ---
|
|
|
| ## 🛠 Model Architecture Details
|
|
|
| - **Base Architecture**: LLaMA-style decoder transformer
|
| - **Parameters**: 134,515,008 (134.52M)
|
| - **Layers**: 30
|
| - **Hidden Dimension**: 576
|
| - **Intermediate Dimension**: 1536
|
| - **Attention Heads**: 9 Query Heads, 3 Key/Value Heads (Grouped-Query Attention)
|
| - **Vocabulary Size**: 49,152
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| - **Context Length**: 2,048 tokens
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| - **Precision**: FP16 / Safetensors (269 MB)
|
|
|
| ---
|
|
|
| ## 📜 Citation & License
|
|
|
| - **License**: Apache 2.0
|
| - **Authors**: Developed & Engineered by `fwefdsfe` (AAA Surgical Engineering Standard).
|
| |
