Create README.md

README.md

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+---
+base_model:
+- nazimali/Mistral-Nemo-Kurdish
+language:
+- ku
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- mistral
+- gguf
+datasets:
+- saillab/alpaca-kurdish_kurmanji-cleaned
+library_name: transformers
+---
+
+This is a 12B parameter model, finetuned on `nazimali/Mistral-Nemo-Kurdish` for a single Kurdish (Kurmanji) instruction dataset. My intention was to train this with both Kurdish Kurmanji Latin script and Kurdish Sorani Arabic script, but training time was much longer than anticipated. 
+So I decided to use 1 full Kurdish Kurmanji dataset to get started.
+
+Will look into a multi-GPU training setup so don't have to wait all day for results. Want to train it with both Kurmanji and Sorani Arabic script.
+
+Try [spaces demo](https://huggingface.co/spaces/nazimali/Mistral-Nemo-Kurdish-Instruct) example.
+
+### Example usage
+
+#### llama-cpp-python
+
+```python
+from llama_cpp import Llama
+
+inference_prompt = """Li jêr rêwerzek heye ku peywirek rave dike, bi têketinek ku çarçoveyek din peyda dike ve tê hev kirin. Bersivek ku daxwazê ​​bi guncan temam dike binivîsin.
+### Telîmat:
+{}
+### Têketin:
+{}
+### Bersiv:
+"""
+
+llm = Llama.from_pretrained(
+	repo_id="nazimali/Mistral-Nemo-Kurdish-Instruct",
+	filename="Q4_K_M.gguf",
+)
+
+llm.create_chat_completion(
+	messages = [
+		{
+			"role": "user",
+			"content": inference_prompt.format("selam alikum, tu çawa yî?")
+		}
+	]
+)
+```
+
+#### llama.cpp
+
+```shell
+./llama-cli \
+  --hf-repo "nazimali/Mistral-Nemo-Kurdish-Instruct" \
+  --hf-file Q4_K_M.gguf \
+  -p "selam alikum, tu çawa yî?" \
+  --conversation
+```
+
+#### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+model_id = "nazimali/Mistral-Nemo-Kurdish-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+)
+```
+
+### Training
+
+#### Finetuning data:
+
+- `saillab/alpaca-kurdish_kurmanji-cleaned`
+- Dataset number of rows: 52,002
+- Filtered columns `instruction, output`
+    - Must have at least 1 character
+    - Must be less than 10,000 characters
+- Number of rows used for training: 41,559
+
+#### Finetuning instruction format:
+
+```python
+finetune_prompt = """Li jêr rêwerzek heye ku peywirek rave dike, bi têketinek ku çarçoveyek din peyda dike ve tê hev kirin. Bersivek ku daxwazê ​​bi guncan temam dike binivîsin.
+### Telîmat:
+{}
+### Têketin:
+{}
+### Bersiv:
+{}
+"""
+```