初始化项目，由ModelHub XC社区提供模型
Model: BUT-FIT/Czech-GPT-2-XL-133k Source: Original Platform
This commit is contained in:
ModelHub XC
94
README.md
Normal file
94
README.md
Normal file
@@ -0,0 +1,94 @@
|
||||
---
|
||||
language:
|
||||
- cs
|
||||
metrics:
|
||||
- perplexity
|
||||
pipeline_tag: text-generation
|
||||
license: mit
|
||||
datasets:
|
||||
- BUT-FIT/BUT-LCC
|
||||
---
|
||||
# Czech GPT
|
||||
This is our GPT-2 XL trained as a part of the research involved in [SemANT project](https://www.fit.vut.cz/research/project/1629/.en).
|
||||
|
||||
# <span style="color:red">BUT LM Model Roster</span>
|
||||
- [BUT-FIT/CSTinyLlama-1.2B](https://huggingface.co/BUT-FIT/CSTinyLlama-1.2B)
|
||||
- [BUT-FIT/Czech-GPT-2-XL-133k](https://huggingface.co/BUT-FIT/Czech-GPT-2-XL-133k)
|
||||
- [BUT-FIT/csmpt7b](https://huggingface.co/BUT-FIT/csmpt7b)
|
||||
|
||||
## Factsheet
|
||||
- The model is trained on our `15,621,685,248 token/78,48 GB/10,900,000,000 word/18,800,000 paragraph` corpus of Czech obtained by Web Crawling.
|
||||
- The original size of our corpus before deduplication and lm-filtering steps was `266,44 GB`.
|
||||
- Our tokenizer size is 64k, and we use GPT-2 like BPE encoding for tokenization.
|
||||
- The model is trained in GPT-2 style, the first token is an actual text token (not bos). Thus first token probability can't be computed.
|
||||
- Due to the feature of our code, our model was never trained to generate [EOS].
|
||||
- The model was trained by 133,000 update steps (~139B training tokens), before the end of the experiment.
|
||||
- The model was adapted from the original GPT-2 XL, by:
|
||||
- replacing the tokenizer,
|
||||
- corresponding embeddings, and
|
||||
- copying over 1,000 EN representations corresponding to the 1,000 most frequent tokens into new embeddings based on a bilingual dictionary.
|
||||
- The training loss decreased steadily, and the model definitely didn't converge yet. We compare the loss to a small 124M model version.
|
||||
<img src="XL_vs_SMALL_train.png" width="600"/>
|
||||
- The validation loss also decreased steadily. We had a bug in validation for early/late steps, so we released only validation from steps 46,000 to 100,000. Similarly, we compare the loss to the small 124M model version.
|
||||
<img src="XL_vs_SMALL_test.png" width="600"/>
|
||||
|
||||
## Training parameters
|
||||
Not mentioned parameters are the same as for GPT-2.
|
||||
|
||||
| **Name** | **Value** | **Note** |
|
||||
|----------------------------|---------------|----------------------------------------------------------------------------------------------|
|
||||
| dataset_type | Concat | Sequences at the model's input were concatenated up to `$max_seq_len`, divided by EOS token. |
|
||||
| tokenizer_size | 64k | |
|
||||
| max_seq_len | 1024 | |
|
||||
| batch_size | 1024 | |
|
||||
| learning_rate | 1.0e-4 | |
|
||||
| optimizer | LionW | |
|
||||
| optimizer_betas | 0.9/0.95 | |
|
||||
| optimizer_weight_decay | 0 | |
|
||||
| optimizer_eps | 1.0e-08 | |
|
||||
| gradient_clipping_max_norm | 1.0 | |
|
||||
| attn_impl | flash2 | |
|
||||
| dropout | 0.1 | for residuals, attention, embeddings |
|
||||
| fsdp | SHARD_GRAD_OP | (optimized for A100 40GB GPUs) |
|
||||
| precision | bf16 | |
|
||||
| scheduler | linear | |
|
||||
| scheduler_warmup | 10,000 steps | |
|
||||
| scheduler_steps | 200,000 | |
|
||||
| scheduler_alpha | 0.1 | So LR on last step is 0.1*(vanilla LR) |
|
||||
|
||||
## Usage
|
||||
```python
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
|
||||
t = AutoTokenizer.from_pretrained("BUT-FIT/Czech-GPT-2-XL-133k")
|
||||
m = AutoModelForCausalLM.from_pretrained("BUT-FIT/Czech-GPT-2-XL-133k").eval()
|
||||
|
||||
# Try the model inference
|
||||
prompt = "Nejznámějším českým spisovatelem "
|
||||
input_ids = t.encode(prompt, return_tensors="pt")
|
||||
with torch.no_grad():
|
||||
generated_text = m.generate(input_ids=input_ids,
|
||||
do_sample=True,
|
||||
top_p=0.95,
|
||||
repetition_penalty=1.0,
|
||||
temperature=0.8,
|
||||
max_new_tokens=64,
|
||||
num_return_sequences=1)
|
||||
print(t.decode(generated_text[0], skip_special_tokens=True))
|
||||
```
|
||||
|
||||
## Evaluation
|
||||
We observed 10-shot result improvement over the course of training for sentiment analysis, and hellaswag-like commonsense reasoning.
|
||||
There were some tasks where there was no such improvement, such as grammar error classification (does the sentence contain grammatical error?).
|
||||
We will release the precise results once we advance with the work on our Czech evaluation kit.
|
||||
|
||||
|
||||
## Disclaimer
|
||||
This is an intermediate result of our work-in-progress. This is a probabilistic model, and authors are not responsible for the model outputs. Use at your own risk.
|
||||
For further questions, turn to `martin.fajcik@vut.cz`.
|
||||
|
||||
## Acknowledgement
|
||||
This work was supported by NAKI III program of Ministry of Culture Czech Republic, project semANT ---
|
||||
"Sémantický průzkumník textového kulturního dědictví" grant no. `DH23P03OVV060` and
|
||||
by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:`90254`).
|
||||
Reference in New Issue
Block a user