OpenR1-Qwen-7B-Turkish

5
21
3 languages
license:apache-2.0
by
WiroAI
Language Model
OTHER
7B params
New
5 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

This is a finetune of Qwen2.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🐨 Quick startpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenR1-Qwen-7B-Turkish"

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

prompt = "$4x+5 = 6x+7$ denklemini sağlayan $x$ değerini bul."

messages = [
    {"role": "system", "content": "Lütfen adım adım düşün ve cevapla."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

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