Llama-3.1_OpenScholar-8B-AWQ

4
3
llama
by
NeuML
Language Model
OTHER
8B params
New
4 downloads
Early-stage
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Mobile
Laptop
Server
18GB+ RAM
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Laptop
Server
Quick Summary

This is Llama-3.1OpenScholar-8B with AWQ Quantization applied using the following code.

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-3.1_OpenScholar-8B-AWQ 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

Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')
Input and output pathpythontransformers
import torch

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

# Input and output path
path = "OpenScholar/Llama-3.1_OpenScholar-8B"
output = "Llama-3.1_OpenScholar-8B-AWQ"

# Quantization config
config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}

# Load model
model = AutoAWQForCausalLM.from_pretrained(
    model_path=path,
    low_cpu_mem_usage=True,
    use_cache=False,
    safetensors=False,
    device_map="cuda",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(path)

# Quantize
model.quantize(tokenizer, quant_config=config)

# Save quantized model
model.save_quantized(output)

# Save tokenizer
# Note: Transformers >= 4.45.0 doubles size of tokenizer.json
# See https://github.com/huggingface/transformers/issues/34744
tokenizer.save_pretrained(output)

print(f'Model is quantized and saved to "{output}"')

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