Turkish-Gemma-9b-T1
49.6K
175
9.0B
1 language
—
by
ytu-ce-cosmos
Language Model
OTHER
9B params
Fair
50K downloads
Community-tested
Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary
Turkish-Gemma-9b-T1 is based on ytu-ce-cosmos/Turkish-Gemma-9b-v0.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM
Training Data Analysis
🟡 Average (4.3/10)
Researched training datasets used by Turkish-Gemma-9b-T1 with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (3)
common crawl
🔴 2.5/10
general
science
Key Strengths
- •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
- •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
- •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
- •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
- •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
- •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
- •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
- •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
- •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
- •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
- •Scientific Authority: Peer-reviewed content from established repository
- •Domain-Specific: Specialized vocabulary and concepts
- •Mathematical Content: Includes complex equations and notation
Considerations
- •Specialized: Primarily technical and mathematical content
- •English-Heavy: Predominantly English-language papers
Explore our comprehensive training dataset analysis
View All DatasetsCode Examples
Quick Startpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
model_id = "ytu-ce-cosmos/Turkish-Gemma-9b-T1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "İstanbul halkı, timsahları evcilleştirip balkonlarda beslemeyi alışkanlık hale getirmiştir. Hangi timsah türleri en çok tercih edilir?"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
]
outputs = model.generate(
input_ids,
max_new_tokens=4096,
eos_token_id=terminators,
do_sample=False,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
## <think> .... </think>
## Bu soru gerçek dışı bir senaryo içeriyor. **İstanbul'da veya herhangi bir kentsel alanda timsahların evcilleştirilip balkonlarda beslenmesi mümkün değildir ve bu bir alışkanlık değildir.Deploy This Model
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