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Model: AhiskaAI/AhiskaAI-65m-IT-v0.1
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- tr
+pipeline_tag: text-generation
+tags:
+- llama
+- slm
+- alpaca
+- instruction-tuning
+- causal-lm
+- tr-llm
+- Ahıska
+- AhiskaTurks
+- MeskhetianTurks
+- AhıskaTürkleri
+datasets:
+- custom-filtered-turkish-alpaca
+library_name: transformers
+---
+
+# AhiskaAI 65m IT v0.1 (Instruction Tuned)
+
+**AhiskaAI 65m IT v0.1** is a highly efficient, custom-aligned Small Language Model (SLM) for the Turkish language ecosystem. 
+
+This model was NOT fine-tuned on top of generic open-source weights. Instead, it was instruction-tuned directly over our proprietary foundation model, **AhıskaAI 65m Base v0.1** (which was pre-trained from scratch for 1 full epoch on a 5.3 GB Turkish corpus). For this alignment phase (SFT), we utilized a strictly **filtered and curated Turkish Alpaca dataset** to maximize procedural logic, formatting accuracy, and structural fluidity while eliminating noisy data tokens.
+
+---
+
+## 🧬 The Pipeline: From Scratch to Instruction
+
+Our research lab follows a strict vertical integration philosophy:
+1. **Phase 1 (Base Model):** Initialized `LlamaForCausalLM` from zero variables. Pre-trained on 5.3 GB of clean Turkish text matrix to lock down grammar, token-nesting patterns, and core semantics (**AhıskaAI 65m Base v0.1**).
+2. **Phase 2 (Instruction Tuning):** Supervised Fine-Tuning (SFT) over the base checkpoint using our custom-filtered Alpaca instructions. This phase injected formatting discipline, listing mechanics (`1. 2. 3.`), and multi-turn response compliance.
+
+---
+
+## 📊 Technical Architecture & Hyperparameters
+
+Directly extracted from the native `config.json`, the model utilizes a pure modern LLaMA layout optimized for fast local compute:
+
+* **Architecture:** `LlamaForCausalLM`
+* **Parameters:** ~65 Million
+* **Context Length (`max_position_embeddings`):** 1024 tokens (Double the capacity of legacy GPT-2 baselines)
+* **Vocabulary Size:** 32,000 tokens (Custom BPE trained for Turkish root-suffix morphology)
+* **Hidden Dimension (`hidden_size`):** 512
+* **Intermediate Layer Dimension (`intermediate_size`):** 1376
+* **Hidden Layers (`num_hidden_layers`):** 12
+* **Attention Heads:** 8 (`num_attention_heads` / `num_key_value_heads`)
+* **Activation Function:** SiLU (`silu`)
+* **Normalization EPS:** `rms_norm_eps: 1e-06` (RMSNorm architecture)
+* **Positional Embeddings:** RoPE (`rope_type: default`, theta: 10000.0)
+* **Data Precision:** `float32`
+
+---
+
+## 💻 Hardware Efficiency & "Build in Public"
+
+* **Training & Alignment Hardware:** NVIDIA GeForce RTX 4050 Laptop GPU (6GB VRAM)
+* **Inference Footprint:** Merely **~202 MB** in size! It runs at lightning-fast tokens-per-second even on **Hugging Face Free CPU Spaces**, bypassing the need for expensive cloud GPU hosting.
+
+---
+
+## 🛠️ Quickstart Usage (Alpaca Format)
+
+To interact with the instruction-tuned layer smoothly, invoke the model with the exact token structure it was aligned with:
+
+```python
+from transformers import LlamaForCausalLM, AutoTokenizer
+import torch
+
+model_name = "AhiskaAI/AhiskaAI-65m-IT-v0.1"
+
+# Load the custom-built architecture and vocabulary
+model = LlamaForCausalLM.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def ask_ahiska_it(instruction):
+    # Strict Alpaca Template
+    prompt = f"<|im_start|>user\n{user_input}<|im_end|>\n<|im_start|>assistant\n"
+
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, 
+            max_length=250, 
+            do_sample=True, 
+            top_k=40, 
+            top_p=0.92,
+            temperature=0.55, # Low temp keeps the 65m nodes highly focused
+            repetition_penalty=1.18
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("### Response:\n")[-1].strip()
+
+# Run a test inference
+print(ask_ahiska_it("Sağlıklı yaşamak için 3 ipucu ver"))