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Model: nazimali/Mistral-Nemo-Kurdish-Instruct Source: Original Platform
2026-09-28 08:05:19 +08:00
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+---
+base_model:
+- nazimali/Mistral-Nemo-Kurdish
+base_model_relation: finetune
+language:
+- ku
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- mistral
+- gguf
+datasets:
+- saillab/alpaca-kurdish_kurmanji-cleaned
+library_name: transformers
+---
+
+<div dir="auto" align="right">
+ئەمە مۆدێلێکی پارامێتری 12B یە، وردکراوە لەسەر نازیماڵی/میستراڵ-نیمۆ-کوردی بۆ یەک داتا سێتی ڕێنمایی کوردی (کرمانجی). مەبەستم ئەوە بوو کە ئەمە بە هەردوو ڕێنووسی کوردی کرمانجی لاتینی و کوردی سۆرانی عەرەبی ڕابهێنم، بەڵام کاتی ڕاهێنان زۆر لەوە زیاتر بوو کە پێشبینی دەکرا. بۆیە بڕیارمدا 1 داتا سێتی کوردی کورمانجی تەواو بەکاربهێنم بۆ دەستپێکردن.
+
+سەیری ڕێکخستنی ڕاهێنانی فرە GPU دەکات بۆیە پێویست ناکات بە درێژایی ڕۆژ چاوەڕێی ئەنجامەکان بکەیت. دەتەوێت بە هەردوو ڕێنووسی عەرەبی کرمانجی و سۆرانی ڕاهێنانی پێبکەیت.
+
+نموونەی دیمۆی بۆشاییەکان تاقی بکەرەوە.
+</div>
+
+
+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("سڵاو ئەلیکوم، چۆنیت؟")
+		}
+	]
+)
+```
+
+#### 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
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+infer_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:
+"""
+
+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",
+)
+
+model.eval()
+
+
+def call_llm(user_input, instructions=None):
+    instructions = instructions or "tu arîkarek alîkar î"
+    prompt = infer_prompt.format(instructions, user_input)
+
+    input_ids = tokenizer(
+        prompt,
+        return_tensors="pt",
+        add_special_tokens=False,
+        return_token_type_ids=False,
+    ).to("cuda")
+
+    with torch.inference_mode():
+        generated_ids = model.generate(
+            **input_ids,
+            max_new_tokens=120,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.7,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    decoded_output = tokenizer.batch_decode(generated_ids)[0]
+
+    return decoded_output.replace(prompt, "").replace("</s>", "")
+
+response = call_llm("سڵاو ئەلیکوم، چۆنیت؟")
+print(response)
+```
+
+### Training
+
+Transformers `4.44.2`  
+1 NVIDIA A40  
+Duration 7h 41m 12s  
+
+```json
+{
+  "total_flos": 2225817933447045000,
+  "train/epoch": 0.9998075072184792,
+  "train/global_step": 2597,
+  "train/grad_norm": 1.172538161277771,
+  "train/learning_rate": 0,
+  "train/loss": 0.7774,
+  "train_loss": 0.892096030377038,
+  "train_runtime": 27479.3172,
+  "train_samples_per_second": 1.512,
+  "train_steps_per_second": 0.095
+}
+```
+
+
+#### 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:
+{}
+"""
+```