Gemma Neogenesis 💎🌍🇮🇹
Collection
Datasets and models for Neogenesis: Post-training recipe for improving Gemma 2 for a specific language. Notebook: https://t.ly/iuKdy
•
11 items
•
Updated
•
5
Fabrizio Salmi
youknowwhatAImean
·
AI & ML interests
None yet
Recent Activity
upvoted
a
collection
11 days ago
Gemma Neogenesis 💎🌍🇮🇹
liked
a model
8 months ago
CAMB-AI/MARS5-TTS
liked
a model
8 months ago
llamas-community/LlamaGuard-7b
Organizations
None yet
youknowwhatAImean's activity
upvoted
a
collection
11 days ago
liked
2
models
8 months ago
[bot] Conversion to Parquet
#2 opened 9 months ago
by
parquet-converter
liked
a
Space
9 months ago
liked
a
model
9 months ago
Librarian Bot: Add language metadata for dataset
#1 opened 9 months ago
by
librarian-bot
updated
a
dataset
9 months ago
upvoted
an
article
9 months ago
Article
How to Finetune phi-3 on MacBook Pro
By
•
•
65
updated
a
Space
9 months ago
reacted to
Jaward's
post with 👍
9 months ago
Post
5377
All You need To Know About Phi-3 (Technical Report Walkthrough)
Summary of Summaries:
Phi-3-mini
- Architecture specs: decoder-only transformer, ModelSize: 3.8 billion
parameters, LongRope [ 128K Context length ], Vocab Size [ 32064 ],
trained on 3.3 trillion tokens. at bfloat16.
- Rivals performance to larger models like Mixtral 8x7B and GPT-3.5,
capable of running locally on a smartphone.
- Utilizes high quality training dataset heavily filtered from web data and
llm-generated synthetic data.
- Can be quantized to 4-bits, occupying ≈ 1.8GB of memory.
- Ran natively on iPhone 14 with A16 Bionic chip with inference speed of up
to 12 tokens per second.
Phi-3-small
- Architecture specs: Also decoder-only, 7B parameters, Vocab size [ 100352 ], default context length [ 8k ], Context Length: 8K, Hidden Dimension: 4096, Number of Heads and Layers: Follows 7B class structure.
- Uses tiktoken tokenizer (for enhanced multilingual tokenization)
Phi-3-medium:
- Architecture specs: Also decoder-only, Hidden Dimension: 5120, Number of Heads: 40, Number of Layers: 40, Tokenization: Consistent with other models, Training on 4.8 trillion tokens.
Training Methodology:
- Focuses on high-quality training data deviating from standard scaling laws.
- The models undergo two-phase pre-training using a mix of web sources and synthetic data for general knowledge and logical reasoning skills.
Performance:
- Phi-3-mini achieves competitive scores on standard benchmarks like MMLU and MT-Bench, indicating strong reasoning capabilities.
- Higher variants show even better performance, suggesting effective scaling with increased model size.
Limitations:
- phi-3-mini: limited by its smaller size in tasks requiring extensive factual knowledge, primarily supports English.
- phi-3-small limited multilingual support.
Hosting LLMs locally is a big win for OSS - private, secured inferencing on the go😎
Summary of Summaries:
Phi-3-mini
- Architecture specs: decoder-only transformer, ModelSize: 3.8 billion
parameters, LongRope [ 128K Context length ], Vocab Size [ 32064 ],
trained on 3.3 trillion tokens. at bfloat16.
- Rivals performance to larger models like Mixtral 8x7B and GPT-3.5,
capable of running locally on a smartphone.
- Utilizes high quality training dataset heavily filtered from web data and
llm-generated synthetic data.
- Can be quantized to 4-bits, occupying ≈ 1.8GB of memory.
- Ran natively on iPhone 14 with A16 Bionic chip with inference speed of up
to 12 tokens per second.
Phi-3-small
- Architecture specs: Also decoder-only, 7B parameters, Vocab size [ 100352 ], default context length [ 8k ], Context Length: 8K, Hidden Dimension: 4096, Number of Heads and Layers: Follows 7B class structure.
- Uses tiktoken tokenizer (for enhanced multilingual tokenization)
Phi-3-medium:
- Architecture specs: Also decoder-only, Hidden Dimension: 5120, Number of Heads: 40, Number of Layers: 40, Tokenization: Consistent with other models, Training on 4.8 trillion tokens.
Training Methodology:
- Focuses on high-quality training data deviating from standard scaling laws.
- The models undergo two-phase pre-training using a mix of web sources and synthetic data for general knowledge and logical reasoning skills.
Performance:
- Phi-3-mini achieves competitive scores on standard benchmarks like MMLU and MT-Bench, indicating strong reasoning capabilities.
- Higher variants show even better performance, suggesting effective scaling with increased model size.
Limitations:
- phi-3-mini: limited by its smaller size in tasks requiring extensive factual knowledge, primarily supports English.
- phi-3-small limited multilingual support.
Hosting LLMs locally is a big win for OSS - private, secured inferencing on the go😎
- 4 replies
updated
a
dataset
9 months ago
Update README.md
#7 opened 10 months ago
by
youknowwhatAImean
