TinyLlama-Sakha-Instruct
1
1 language
llama
by
lab-ii
Language Model
OTHER
New
1 downloads
Early-stage
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Mobile
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Quick Summary
AI model with specialized capabilities.
Training Data Analysis
🟡 Average (4.8/10)
Researched training datasets used by TinyLlama-Sakha-Instruct with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (4)
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...
c4
🔵 6/10
general
multilingual
Key Strengths
- •Scale and Accessibility: 750GB of publicly available, filtered text
- •Systematic Filtering: Documented heuristics enable reproducibility
- •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
- •English-Only: Limits multilingual applications
- •Filtering Limitations: Offensive content and low-quality text remain despite filtering
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
How to usepythontransformers
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="lab-ii/TinyLlama-Sakha-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
prompt_input = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n\n{instruction}\n\n### Response:\n\n"
)
raw_input_text = "Доруобай буолар кына үс сүбэни биэр"
promnt = generate_prompt(instruction=raw_input_text)
outputs = pipe(promnt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Доруобай буолар кына үс сүбэни биэр
### Response:
1. Аһылыккын тутус уонна элбэх фруктаны уонна хортуоппуйу сиэ.
2. Этиҥ-сииниҥ көхтөөх уонна күүстээх буоларын туһугар өрүү дьарыктан.
3. Ситэри утуй уонна биир тэҥ утуйар графигы тутус.Deploy This Model
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