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1base_model: meta-llama/Meta-Llama-3-8B
2
3models:
4 - model: meta-llama/Meta-Llama-3-8B # Base model (retain strong foundational performance)
5 parameters:
6 weight: 1.0
7 - model: ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1 # High-quality Turkish instruction tuning
8 parameters:
9 weight: 0.7 # Increased weight for better instruction following
10 - model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1 # DPO-tuned for Turkish preferences
11 parameters:
12 weight: 0.5 # Balanced for alignment
13
14# LoRA Adapters (focused on Turkish language/culture)
15adapters:
16 - model: Yudum/llama3-lora-turkish # Core Turkish fine-tuning
17 parameters:
18 weight: 0.8 # Highest priority (language fundamentals)
19 - model: Yudum/Meta-Llama-3-8B-Instruct_tr_wiki_tr_wiki_mix # Turkish Wikipedia knowledge
20 parameters:
21 weight: 0.6 # Boost cultural/encyclopedic knowledge
22 - model: Kasimyildirim/Qwen2-turkish-comnibed-v5-lora # Additional Turkish data mix
23 parameters:
24 weight: 0.4 # Complementary reinforcement
25
26merge_method: ties
27parameters:
28 density: 0.5 # Slightly lower density to reduce noise
29 weight: 0.9 # Stronger weight retention for Turkish features
30 normalize: true
31dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "matrixportal/Turkish-Llama3-8B-Merged"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])