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Model: Rudblest/projedanismanai
Source: Original Platform

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README.md

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+---
+language:
+- tr
+license: apache-2.0
+base_model: mistralai/Mistral-Nemo-Instruct-2407
+tags:
+- mistral
+- turkish
+- teknofest
+- tubitak
+- fine-tuned
+- qlora
+- unsloth
+pipeline_tag: text-generation
+---
+
+# ProjeDanışmanAi
+
+**TEKNOFEST ve TÜBİTAK yarışmacıları için Türkçe yapay zeka danışmanı.**
+
+Mistral-Nemo-Instruct-2407 (12B) modeli, TEKNOFEST ve TÜBİTAK yarışma süreçlerine özel Türkçe veri setiyle fine-tune edilmiştir.
+
+---
+
+## Model Detayları
+
+| Özellik | Değer |
+|---|---|
+| Temel Model | mistralai/Mistral-Nemo-Instruct-2407 |
+| Yöntem | QLoRA 4-bit + unsloth |
+| LoRA Rank | 64 (alpha=128, rsLoRA=True) |
+| Eğitim Verisi | 3043 Türkçe instruction-output çifti |
+| Epoch | 5 |
+| Max Seq Length | 3072 |
+| Train Loss | 0.3591 |
+
+---
+
+## Kullanım Alanları
+
+- TEKNOFEST KTR/PTR teknik rapor yazımı
+- TÜBİTAK başvuru hazırlığı
+- Proje fikri netleştirme
+- Risk analizi ve uygulanabilirlik değerlendirmesi
+- Başlık ve özet üretimi
+- Jüri ve sunum hazırlığı
+
+---
+
+## Veri Seti
+
+3043 Türkçe örnekten oluşan özel veri seti:
+
+| Kategori | Örnek Sayısı |
+|---|---|
+| rapor_yazimi | 761 |
+| sifirdan_proje | 730 |
+| genel_ozet | 593 |
+| strateji | 505 |
+| hata_duzeltme | 324 |
+| red (alan dışı red) | 130 |
+
+---
+
+## Kullanım
+
+### GGUF (Ollama ile)
+
+```bash
+ollama create projedanismanai -f Modelfile
+ollama run projedanismanai
+```
+
+### Python (unsloth ile)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name     = "Rudblest/projedanismanai",
+    max_seq_length = 3072,
+    dtype          = None,
+    load_in_4bit   = True,
+)
+FastLanguageModel.for_inference(model)
+
+prompt = "<s>[INST] TEKNOFEST KTR raporunda risk analizi nasıl yazılır? [/INST] "
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+---
+
+## Sınırlamalar
+
+- Yalnızca Türkçe cevap verir
+- Alan dışı sorular (yemek, borsa, sağlık vb.) reddedilir
+- TEKNOFEST/TÜBİTAK dışı mühendislik konularında performans düşebilir
+
+---
+
+## Lisans
+
+Apache 2.0 — Mistral-Nemo temel modeli lisansına uygun.

config.json

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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "vocab_size": 131072
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "5.5.0"
+}

mistral-nemo-instruct-2407.Q4_K_M.gguf

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}