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Model: AhiskaAI/AhiskaAI-110M-Experimental-v0.1-Instruct
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- tr
+- en
+license: apache-2.0
+tags:
+- instruct
+- sft
+- chatml
+- Turkish
+- llama
+- causal-lm
+- slm
+- experimental
+- tr-llm
+- Ahıska
+- AhiskaTurks
+- MeskhetianTurks
+- AhıskaTürkleri
+datasets:
+- AhiskaAI/AhiskaAI-Instruct-v0.2-Sample-Dataset
+pipeline_tag: text-generation
+---
+
+# AhıskaAI-110M-IT-Experimental-v0.1
+
+**AhıskaAI-110M-IT-Experimental-v0.1** is an instruction-tuned Small Language Model (~110M parameters) fine-tuned from [AhıskaAI-110M-Experimental-v0.1](https://huggingface.co/AhıskaAI/AhıskaAI-110M-Experimental-v0.1). It is designed to evaluate early instruction-following capabilities on top of synthetic pre-trained representations, using multi-turn Turkish conversations with the **ChatML** template.
+
+## Model Highlights
+
+- **Instruction Alignment:** Supervised Fine-Tuned (SFT) using custom-cleaned multi-turn instruction datasets formatted in ChatML.
+- **Loss Masking Strategy:** Trained using custom prompt masking (`labels = -100` for user/system tokens), ensuring loss is calculated **only** on assistant responses for concise and non-hallucinating outputs.
+- **System Prompt Support:** Native support for fixed system prompts prioritizing polite, short, and accurate Turkish answers.
+- **Hardware-Efficient Fine-Tuning:** Trained on consumer-grade hardware (NVIDIA RTX 4050 6GB GPU) using Liger Kernel acceleration (`apply_liger_kernel_to_llama`) and `adamw_torch_fused`.
+
+## Model Details
+
+- **Base Model:** `AhıskaAI/AhıskaAI-110M-Experimental-v0.1`
+- **Architecture:** `LlamaForCausalLM` (MHA)
+- **Parameters:** ~110M
+- **Fine-Tuning Method:** Full Parameter SFT (Supervised Fine-Tuning)
+- **Context Length:** 1024 tokens
+- **Template:** ChatML (`<|im_start|>` and `<|im_end|>`)
+- **Precision:** Bf16
+
+## Supported System Prompts
+
+The model has been optimized around two primary system personas:
+
+1. `"Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."`
+2. `"Sen AhıskaAI adında Türkçe bir yapay zeka asistansın.\nGörevlerin:\n1. Sorulara doğrudan, net ve kısa cümlelerle cevap ver.\n2. Bilmediğin veya emin olmadığın konularda uydurma yapma, bilmiyorum de.\n3. Kullanıcının verdiği metin veya listeleri istenen formata sadık kalarak düzenle."`
+
+## Usage (with Transformers)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "AhıskaAI/AhıskaAI-110M-IT-Experimental-v0.1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+# ChatML Formatting
+system_prompt = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": "11 sayısından 2 çıkarırsak kaç kalır? Açıkla."}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    temperature=0.3,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Training Parameters & Hardware
+**Epochs:** 1
+**Learning Rate:** 5e-5 (Cosine Schedule)
+**Batch Size:** 32 (effective)
+**Optimizer:** AdamW Fused (adamw_torch_fused)
+**Acceleration:** Liger Kernel (liger-kernel) & SDPA
+**Hardware:** NVIDIA RTX 4050 Laptop GPU (6GB VRAM)
+
+### Related Resources
+**Base Model:** AhıskaAI-110M-Experimental-v0.1
+
+### About AhıskaAI
+**AhıskaAI** is an independent initiative dedicated to developing efficient, high-performance Small Language Models (SLMs) tailored for the Turkish language ecosystem.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 12,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.5.0",
+  "use_cache": false
+}

model.safetensors

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "is_local": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>"
+}