Llama-3.1-8B-tldr-FP8-dynamic

58
1
8.0B
llama
by
RedHatAI
Language Model
OTHER
8B params
New
58 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

Model Overview - Model Architecture: LlamaForCausalLM - Input: Text - Output: Text - Model Optimizations: - Weight quantization: FP8 - Activation quantization:...

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-3.1-8B-tldr-FP8-dynamic 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

pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Llama-3.1-8B-tldr"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True)
tokenizer.save_pretrained(output_dir)

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