pisco-llama

12
3
8.0B
base_model:meta-llama/Llama-3.1-8B-Instruct
by
naver
Code Model
OTHER
8B params
New
12 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

PISCO is a context compression model to be used for efficient inference when doing Retrieval Augmented Generation (RAG), particularly optimized for question answering.

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 pisco-llama 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

Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)
Usagepythontransformers
from transformers import AutoModel

pisco = AutoModel.from_pretrained('naver/pisco-llama').to('cuda')

# Example documents and question:
documents = [
    [
        "Weldenia is a monotypic genus of flowering plant in the family Commelinaceae, first describ ed in 1829. It has one single species: Weldenia candida, which grows originally in Mexico and Guatemala.",
        "Hagsatera is a genus of flowering plants from the orchid family, Orchidaceae. There are two known species, native to Mexico and Guatemala",
        "Alsobia is a genus of flowering plants in the family Gesneriaceae, native to Mexico, Guatemala and Costa Rica. The two species are succulent, stoloniferous herbs and were previously included in the genus \"Episcia\". Recent molecular studies have supported the separation of \"Alsobia\" from \"Episcia\""
    ]
]

questions = ["Which genus of plant grows originally in Mexico and Guatemala, Phylica or Weldenia?"]

# End-to-end usage
out = pisco.generate_from_text(questions=questions, documents=documents, max_new_tokens=64)
print('Generated answer', out)

# Document compression:
embeddings = pisco.compress_documents(documents=documents[0])

# Generation from compressed documents:
out = pisco.generate_from_compressed_documents_and_questions(questions=questions, compressed_documents=embeddings)

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