|
--- |
|
language: |
|
- en |
|
- zh |
|
license: mit |
|
datasets: |
|
- wenbopan/Chinese-dpo-pairs |
|
- Intel/orca_dpo_pairs |
|
- argilla/ultrafeedback-binarized-preferences-cleaned |
|
- jondurbin/truthy-dpo-v0.1 |
|
pipeline_tag: text-generation |
|
--- |
|
|
|
# Dataset Card for Faro-Yi-9B-DPO |
|
|
|
This is the DPO version of [wenbopan/Faro-Yi-9B](https://huggingface.co/wenbopan/Faro-Yi-9B). Compared to Faro-Yi-9B and [Yi-9B-200K](https://huggingface.co/01-ai/Yi-9B-200K), the DPO model excels at many tasks, surpassing the original Yi-9B-200K by a large margin. |
|
|
|
| **Metric** | **MMLU** | GSM8K | **hellaswag** | **truthfulqa** | **ai2_arc** | **winogrande** | **CMMLU** | |
|
| --------------- | --------- | --------- | ------------- | -------------- | ----------- | -------------- | --------- | |
|
| **Yi-9B-200K** | 65.73 | 50.49 | 56.72 | 33.80 | 69.25 | 71.67 | 71.97 | |
|
| **Faro-9B** | **68.80** | **63.08** | 57.28 | 40.86 | 72.58 | 71.11 | **73.28** | |
|
| **Faro-9B-DPO** | 66.96 | 57.92 | **61.14** | **51.63** | **75.25** | **75.53** | 71.02 | |
|
|
|
|
|
 |
|
|
|
## How to Use |
|
|
|
Faro-Yi-9B-DPO uses the chatml template and performs well in both short and long contexts. For longer inputs under **24GB of VRAM**, I recommend to use vLLM to have a max prompt of 32K. Setting `kv_cache_dtype="fp8_e5m2"` allows for 48K input length. 4bit-AWQ quantization on top of that can boost input length to 160K, albeit with some performance impact. Adjust `max_model_len` arg in vLLM or `config.json` to avoid OOM. |
|
|
|
|
|
```python |
|
import io |
|
import requests |
|
from PyPDF2 import PdfReader |
|
from vllm import LLM, SamplingParams |
|
|
|
llm = LLM(model="wenbopan/Faro-Yi-9B-DPO", kv_cache_dtype="fp8_e5m2", max_model_len=100000) |
|
|
|
pdf_data = io.BytesIO(requests.get("https://arxiv.org/pdf/2303.08774.pdf").content) |
|
document = "".join(page.extract_text() for page in PdfReader(pdf_data).pages) # 100 pages |
|
|
|
question = f"{document}\n\nAccording to the paper, what is the parameter count of GPT-4?" |
|
messages = [ {"role": "user", "content": question} ] # 83K tokens |
|
prompt = llm.get_tokenizer().apply_chat_template(messages, add_generation_prompt=True, tokenize=False) |
|
output = llm.generate(prompt, SamplingParams(temperature=0.8, max_tokens=500)) |
|
print(output[0].outputs[0].text) |
|
# Yi-9B-200K: 175B. GPT-4 has 175B \nparameters. How many models were combined to create GPT-4? Answer: 6. ... |
|
# Faro-Yi-9B: GPT-4 does not have a publicly disclosed parameter count due to the competitive landscape and safety implications of large-scale models like GPT-4. ... |
|
``` |
|
|
|
|
|
<details> <summary>Or With Transformers</summary> |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
model = AutoModelForCausalLM.from_pretrained('wenbopan/Faro-Yi-9B-DPO', device_map="cuda") |
|
tokenizer = AutoTokenizer.from_pretrained('wenbopan/Faro-Yi-9B-DPO') |
|
messages = [ |
|
{"role": "system", "content": "You are a helpful assistant. Always answer with a short response."}, |
|
{"role": "user", "content": "Tell me what is Pythagorean theorem like you are a pirate."} |
|
] |
|
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) |
|
generated_ids = model.generate(input_ids, max_new_tokens=512, temperature=0.5) |
|
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True) # Aye, matey! The Pythagorean theorem is a nautical rule that helps us find the length of the third side of a triangle. ... |
|
``` |
|
|
|
</details> |
|
|
