mueggi
/

ka-ai-instruct

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 ---
 base_model: google/gemma-2-9b
-library_name: peft
 pipeline_tag: text-generation
-tags:
-- base_model:adapter:google/gemma-2-9b
-- lora
-- transformers
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
+language:
+  - ka
+  - en
+license: apache-2.0
+tags:
+  - georgian
+  - gemma2
+  - lora
+  - sft
+  - instruction-tuned
 base_model: google/gemma-2-9b
 pipeline_tag: text-generation
 ---
+# ka-ai-instruct — Georgian Instruction-Tuned LLM
+The first open-source instruction-tuned LLM optimized for Georgian language.
 ## Model Details
+- **Base model**: Google Gemma 2 9B
+- **Training**: Continual pretraining on 5M Georgian documents + SFT on 15K instruction examples
+- **Architecture**: QLoRA adapter (r=32, 4 attention modules)
+- **Tokenizer**: Extended with +10K Georgian tokens (265K total vocab)
+- **Training loss**: 0.92
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+# Load base + adapter
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
+base = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b", quantization_config=bnb_config, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("mueggi/ka-ai-tokenizer")
+base.resize_token_embeddings(len(tokenizer), mean_resizing=False)
+model = PeftModel.from_pretrained(base, "mueggi/ka-ai-instruct")
+# Chat
+messages = [{"role": "user", "content": "რა არის საქართველოს დედაქალაქი?"}]
+template = "{% for message in messages %}<|im_start|>{{ message.role }}\n{{ message.content }}<|im_end|>\n{% endfor %}<|im_start|>assistant\n"
+tokenizer.chat_template = template
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7,
+                     stop_strings=["<|im_end|>"], tokenizer=tokenizer)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+## Capabilities
+| Task | Quality |
+|------|---------|
+| Georgian QA | ⭐⭐⭐⭐ |
+| Georgian fluency | ⭐⭐⭐⭐ |
+| KA→EN translation | ⭐⭐⭐⭐ |
+| EN→KA translation | ⭐⭐ |
+| Creative writing | ⭐⭐⭐⭐ |
+## Training Data
+- **Pretraining**: 5.1M Georgian documents (C4, HPLT, Wikipedia, news)
+- **SFT**: 15K examples sampled from 103K (hand-crafted + Kona SFT + translation pairs)
+- **Format**: ChatML
+## Limitations
+- Trained with max 256 token sequences (hardware constraint) — may truncate long responses
+- EN→KA translation needs improvement
+- Can hallucinate facts
+- Stop token (`<|im_end|>`) requires `stop_strings` parameter in generation
+## Future
+- Full 103K SFT dataset with 1024 seq_len (pending better hardware)
+- DPO alignment with 387K preference pairs
+- Larger base models (27B, 70B)
+## License
+Apache 2.0 (same as Gemma 2)

generation_config.json ADDED Viewed

	@@ -0,0 +1,14 @@

+{
+  "_from_model_config": false,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "max_new_tokens": 512,
+  "do_sample": true,
+  "temperature": 0.7,
+  "top_p": 0.9,
+  "repetition_penalty": 1.1,
+  "stop_strings": [
+    "<|im_end|>"
+  ],
+  "transformers_version": "4.48.0"
+}