Quantization made by Richard Erkhov.
KoSOLAR-10.7B-v0.2 - bnb 4bits
Original model description:
license: apache-2.0
base_model: upstage/SOLAR-10.7B-v1.0
tags:
- generated_from_trainer
model-index:
- name: yanolja/KoSOLAR-10.7B-v0.2
results: []
KoSOLAR-10.7B-v0.2
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Discord Link.
Our Dedicated Team (Alphabetical Order)
| Research | Engineering | Product Management | UX Design |
|---|
| Myeongho Jeong | Geon Kim | Bokyung Huh | Eunsue Choi |
| Seungduk Kim | Rifqi Alfi | | |
| Seungtaek Choi | Sanghoon Han | | |
| Suhyun Kang | | |
About the Model
This model is a Korean vocabulary-extended version of
upstage/SOLAR-10.7B-v1.0, specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the
lm_head embeddings for the already existing tokens while preserving the original parameters of the base model.
Technical Deep Dive
Here’s a glimpse into our technical approach:
1def freeze_partial_embedding_hook(grad):
2 grad[:32000] = 0
3 return grad
4
5for name, param in model.named_parameters():
6 if ("lm_head" in name or "embed_tokens" in name) and "original" not in name:
7 param.requires_grad = True
8 if "embed_tokens" in name:
9 param.register_hook(freeze_partial_embedding_hook)
10 else:
11 param.requires_grad = False
Our strategy involved a selective freeze of model parameters. Specifically, we kept most parameters of the base model unchanged while focusing on enhancing the Korean language capabilities. Through our experiments, we discovered:
- Freezing the
embed_tokens layer for existing tokens is crucial to maintain overall performance.
- Unfreezing the
lm_head layer for existing tokens actually boosts performance.
As a result, we froze the internal layers and the first 32,000 embed_tokens, directing our training efforts on a rich mix of Korean and multi-lingual corpora. This balanced approach has notably improved the model’s proficiency in Korean, without compromising its original language capabilities.
Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
Training Details
Our model’s training was comprehensive and diverse:
-
Data Sources:
- English to Korean paragraph pairs: 5.86%
- Multi-lingual corpus (primarily English): 10.69%
- Korean web content: 83.46%
-
Vocabulary Expansion:
We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.
-
Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.
-
Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.
-
Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens.
-
Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.
-
Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.
-
Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.
-
Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add.
-
Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning.
This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
