Skip to content
ModelListedExternal
Browse files

Sorjin1 7B

Sorjîn1-7B is a Kurdish Kurmanji language model produced by continually pretraining Qwen/Qwen2.5-7B on academic theses written in Kurdish Kurmanji.

KindOtherBase modelQwen/Qwen2.5-7BVersionRevision 97102eeCgrade

Model source

Kind
Other
File size
1.02–4.59 GB
Base model
Qwen/Qwen2.5-7B
Version
vRevision 97102ee
Updated
Sep 26, 2026
Downloads
10
Source
Hugging Face

Source description

Sorjîn1-7B

Sorjîn1-7B is a Kurdish Kurmanji language model produced by continually pretraining Qwen/Qwen2.5-7B on academic theses written in Kurdish Kurmanji.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "muzaffercky/Sorjin1-7B",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("muzaffercky/Sorjin1-7B")

inputs = tokenizer("Zimanê kurdî zimanekî", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Evaluation

ModelEval LossPerplexity
Qwen/Qwen2.5-7B (base)3.4230.57
Sorjîn1-7B1.866.42

5× perplexity reduction after a single epoch of continual pretraining.

Training

  • Base model: Qwen/Qwen2.5-7B
  • Dataset: muzaffercky/kurdish-kurmanji-theses — 389 academic theses, 57.6 MB of cleaned Kurmanji text
  • Method: QLoRA continual pretraining (4-bit NF4 quantization during training, merged to bfloat16)
  • LoRA rank: 64, alpha: 128, target modules: all attention and MLP projections
  • Sequence length: 2048 tokens
  • Epochs: 1
  • Hardware: 1× NVIDIA RTX 4090

Intended Use

This is a base model — it is not instruction-tuned. It is intended for:

  • Further fine-tuning on Kurdish downstream tasks
  • Merging with instruction-tuned models (see Sorjîn1-7B-Instruct)
  • Kurdish language research

Limitations

  • Trained on academic theses only — language style is formal and academic
  • Single epoch training — further pretraining on broader Kurdish data would improve general language coverage
  • Not suitable for direct use as a chat assistant — use Sorjîn1-7B-Instruct instead

Model Family

ModelDescription
Sorjîn1-LoRARaw LoRA adapter weights
Sorjîn1-7B (this model)Continually pretrained base model
Sorjîn1-7B-InstructMerged with Qwen2.5-7B-Instruct

Sources

1 source
Hugging FacePrimaryVersion 97102ee75fa06ca12d5521baefdc05d8ab7eb8d3
huggingface.co/muzaffercky/Sorjin1-7B/tree/97102ee75fa06ca12d5521baefdc05d8ab7eb8d3

Verified Sep 7

Download files

Model artifacts

4 artifacts

model-00001-of-00004.safetensors

safetensors · 4.54 GB · SHA-256 3e8a1200f67f…5855 · Hugging Face

Download shard

model-00002-of-00004.safetensors

safetensors · 4.59 GB · SHA-256 c5713484be3f…5c17 · Hugging Face

Download shard

model-00003-of-00004.safetensors

safetensors · 4.03 GB · SHA-256 1b984b369073…d107 · Hugging Face

Download shard

model-00004-of-00004.safetensors

safetensors · 1.02 GB · SHA-256 5be8567a86b9…31c5 · Hugging Face

Download shard

Source excerpts

2 excerpts

--- language: - ku - kmr base_model: Qwen/Qwen2.5-7B tags: - kurdish - kurmanji - continual-pretraining - lora - qwen2.5 license: apache-2.0 datasets: - muzaffercky/kurdish-kurmanji-theses --- # Sorjîn1-7B Sorjîn1-7B is a Kurdish Kurmanji language model produced by continually pretraining [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on academic theses written in Kurdish Kurmanji. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "muzaffercky/Sorjin1-7B", torch_dtype="bfloat16", device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("muzaffercky/Sorjin1-7B") inputs = tokenizer("Zimanê kurdî zimanekî", return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ## Evaluation | Model | Eval Loss | Perplexity | |-------|-----------|------------| | Qwen/Qwen2.5-7B (base) | 3.42 | 30.57 | | Sorjîn1-7B | 1.86 | 6.42 | 5× perplexity reduction after a single epoch of continual pretraining. ## Training - **Base model:** [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) - **Dataset:** [muzaffercky/kurdish-kurmanji-theses](https://huggingface.co/datasets/muzaffercky/kurdish-kurmanji-theses) — 389 academic theses, 57.6 MB of cleaned Kurmanji text - **Method:** QLoRA continual pretraining (4-bit NF4 quantization during training, merged to bfloat16) - **LoRA rank:** 64, alpha: 128, target modules: all attention and MLP projections - **Sequence length:** 2048 tokens - **Epochs:** 1 - **Hardware:** 1× NVIDIA RTX 4090 ## Intended Use This is a **base model** — it is not instruction-tuned. It is intended for: - Further fine-tuning on Kurdish downstream tasks - Merging with instruction-tuned models (see [Sorjîn1-7B-Instruct](https://huggingface.co/muzaffercky/Sorjin1-7B-Instruct)) - Kurdish language research ## Limitations - Trained on academic theses only — language style is formal and academic - Single epoch training — further pretraining on broader Kurdish data would improve general language coverage - Not suitable for direct use as a chat assistant — use Sorjîn1-7B-Instruct instead ## Model Family | Model | Description | |-------|-------------| | [Sorjîn1-LoRA](https://huggingface.co/muzaffercky/Sorjin1-LoRA) | Raw LoRA adapter weights | | Sorjîn1-7B (...

Source context: 10 downloads · 0 likes · Repo muzaffercky/Sorjin1-7B