SvS-LLama-8B

4
llama
by
RLVR-SvS
Other
OTHER
8B params
New
4 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by SvS-LLama-8B 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 Datasets

Code Examples

Inferencepython
model_name = "RLVR-SvS/SvS-LLama-8B"
device = "cuda" # the device to load the model onto

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

prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."

messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"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,
    max_new_tokens=8192
)
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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