Phi-4-mini-instruct-parq-4w-4e-shared-gsm

53
license:mit
by
pytorch
Language Model
OTHER
New
53 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🟡 Average (5.2/10)

Researched training datasets used by Phi-4-mini-instruct-parq-4w-4e-shared-gsm with quality assessment

Specialized For

code
general
science
multilingual

Training Datasets (3)

the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
  • •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
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...
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 ...

Explore our comprehensive training dataset analysis

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Code Examples

text
git clone https://github.com/pytorch/executorch.git                   
pushd executorch           
git submodule update --init --recursive 
python install_executorch.py
USE_CPP=1 TORCHAO_BUILD_KLEIDIAI=1 pip install third-party/ao
popd
1. Download QAT'd weights from HFbash
# 1. Download QAT'd weights from HF
HF_DIR=pytorch/Phi-4-mini-instruct-parq-4w-4e-shared-gsm
WEIGHT_DIR=$(hf download ${HF_DIR})

# 2. Rename the weight keys to ones that ExecuTorch expects 
python -m executorch.examples.models.phi_4_mini.convert_weights $WEIGHT_DIR pytorch_model_converted.bin

# 3. Download model config from the ExecuTorch repo
curl -L -o phi_4_mini_config.json https://raw.githubusercontent.com/pytorch/executorch/main/examples/models/phi_4_mini/config/config.json

# 4. Export the model to ExecuTorch pte file
python -m executorch.examples.models.llama.export_llama \
  --model "phi_4_mini" \
  --checkpoint pytorch_model_converted.bin \
  --params phi_4_mini_config.json \
  --output_name phi4_model_4bit.pte \
  -kv \
  --use_sdpa_with_kv_cache \
  --use-torchao-kernels \
  --max_context_length 1024 \
  --max_seq_length 256 \
  --dtype fp32 \
  --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}'

# # 5. (optional) Upload pte file to HuggingFace
# hf upload ${HF_DIR} phi4_model_4bit.pte

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