Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3
3
3.0B
llama
by
ModelCloud
Other
OTHER
3B params
New
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Early-stage
Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary
This model was quantized and exported to mlx using GPTQModel.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM
Training Data Analysis
🟡 Average (4.8/10)
Researched training datasets used by Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3 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 DatasetsCode Examples
How to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelbash
# install mlx
pip install mlx_lmHow to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)How to run this modelpython
from mlx_lm import load, generate
mlx_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
mlx_model, tokenizer = load(mlx_path)
prompt = "The capital of France is"
)
text = generate(mlx_model, tokenizer, prompt=prompt, verbose=True)Export gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxbash
# install gptqmodel with mlx
pip install gptqmodel[mlx] --no-build-isolationExport gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Export gptq to mlxpython
from gptqmodel import GPTQModel
# load gptq quantized model
gptq_model_path = "ModelCloud/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-v3"
mlx_path = f"./vortex/Llama-3.2-3B-Instruct-gptqmodel-4bit-vortex-mlx-v3"
# export to mlx model
GPTQModel.export(gptq_model_path, mlx_path, "mlx")Deploy This Model
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