cturan / Lamba-750M

Lamba-750M

Lamba-750M is a compact Turkish language model. It is based on Qwen3.5 and trained specifically for Turkish instruction following.

Intended Use

Designed for short Turkish tasks like classification, summarization, and text transformation. Not recommended for English prompts, long-form generation, math, or multi-step reasoning.

Training

1. Continual Pre-Training (CPT): 10B Turkish tokens to adapt to Turkish grammar and vocabulary.
2. Supervised Fine-Tuning (SFT): Trained on question-answer instructions. Best eval loss: 0.9285 at ~18k steps.
3. DPO: Direct Preference Optimization for better alignment with human preferences.

Capabilities

Small but effective for short Turkish tasks:

• Summarization
• Text classification (sentiment, topic)
• Text transformation (informal to formal, antonyms)
• Short, single-turn instructions

Limitations

• Weak at math, logic, and multi-step reasoning. Do not use Chain-of-Thought, it increases hallucinations.
• Can hallucinate facts on knowledge-heavy topics.
• May repeat phrases in long generations.
• Text-only, no vision support.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "cturan/Lamba-750M"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16"
)

prompt = "Bu yorumun duygusunu tek kelimeyle belirt (olumlu/olumsuz): Ürün çok hızlı geldi, paketleme harikaydı ve tam beklediğim gibi çalışıyor."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))