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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -56,7 +56,7 @@ A demo of using CLLM to achieve significant improvements ($\sim3\times$) in gene
## News 🔥

- [2024/3] CLLMs are integrated in [FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/model_support.md)!
- [2024/2] CLLM Paper now available on [arXiv](http://arxiv.org/abs/2403.00835). CLLMs model checkpoints are released on [Huggingface Hub](https://huggingface.co/cllm).
- [2024/2] CLLM Paper now available on [arXiv](http://arxiv.org/abs/2403.00835). CLLMs model checkpoints are released on [Hugging Face Hub](https://huggingface.co/cllm).

## Introduction
Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders refined from pre-trained LLMs.
Expand Down Expand Up @@ -89,15 +89,15 @@ pip install flash-attn==2.4.1
## Model Weights
#### Target Pre-trained Models

| Size | Dataset | Huggingface Repo |
| Size | Dataset | Hugging Face Repo |
| ---- | -------- | --------------------------------------------- |
| 7B | ShareGPT | [cllm/vicuna-7b-sharegpt-gpt4-48k](https://huggingface.co/cllm/vicuna-7b-sharegpt-gpt4-48k) |
| 7B | GSM8K (Math) | [GAIR/Abel-7B-001](https://huggingface.co/GAIR/Abel-7B-001) |
| 7B | Spider (Text-to-SQL) | [cllm/deepseekcoder-7b-instruct-spider](https://huggingface.co/cllm/deepseekcoder-7b-instruct-spider) |
| 7B | Code-Search-Net Python | [cllm/deepseekcoder_7b_codesearch_net_python](https://huggingface.co/cllm/deepseekcoder_7b_codesearch_net_python) |

#### CLLMs
| Size | Dataset | Huggingface Repo |
| Size | Dataset | Hugging Face Repo |
| ---- | -------- | --------------------------------------------- |
| 7B | ShareGPT | [cllm/consistency-llm-7b-sharegpt48k](https://huggingface.co/cllm/consistency-llm-7b-sharegpt48k) |
| 7B | GSM8K (Math) | [cllm/consistency-llm-7b-math](https://huggingface.co/cllm/consistency-llm-7b-math) |
Expand All @@ -113,8 +113,8 @@ bash applications/run_chat_cllm.sh {model_path} {cllm_type}

### Training
1. Collect Jacobi trajectory:
- Method 1: Directly download Jacobi trajectory to `data/collected_jacobi_trajectory/` from [our Huggingface Hub page](https://huggingface.co/cllm).
- Method 2 (Generate trajectory suitable to your own target model and dataset): Some raw datasets that contain additional information like database dependency or cannot be directly loaded from Huggingface Hub (for example, [Spider](https://huggingface.co/datasets/cllm/spider) and [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json) are required to be installed in `data/raw_data`). Then run `scripts/generate_trajectory.sh` and the training dataset for a CLLM will be saved in `data/collected_jacobi_trajectory/`.
- Method 1: Directly download Jacobi trajectory to `data/collected_jacobi_trajectory/` from [our Hugging Face Hub page](https://huggingface.co/cllm).
- Method 2 (Generate trajectory suitable to your own target model and dataset): Some raw datasets that contain additional information like database dependency or cannot be directly loaded from Hugging Face Hub (for example, [Spider](https://huggingface.co/datasets/cllm/spider) and [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json) are required to be installed in `data/raw_data`). Then run `scripts/generate_trajectory.sh` and the training dataset for a CLLM will be saved in `data/collected_jacobi_trajectory/`.

For example, for the gsm8k dataset, run:
```
Expand Down