Llama-3-ELYZA-JP-8B

Name: Llama-3-ELYZA-JP-8B
Author: elyza

25.3K

135

8.0B

2 languages

llama

elyza

Language Model

OTHER

8B params

Fair

25K downloads

Community-tested

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

18GB+ RAM

Mobile

Laptop

Server

Quick Summary

Llama-3-ELYZA-JP-8B is a large language model trained by ELYZA, Inc.

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 Llama-3-ELYZA-JP-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...

🔵 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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

Usagepythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/Llama-3-ELYZA-JP-8B"

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

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

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