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--- |
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language: |
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- en |
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- zh |
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license: mit |
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datasets: |
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- wenbopan/Chinese-dpo-pairs |
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- Intel/orca_dpo_pairs |
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- argilla/ultrafeedback-binarized-preferences-cleaned |
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- jondurbin/truthy-dpo-v0.1 |
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pipeline_tag: text-generation |
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--- |
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# Vikarti notes |
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EXL2 6bpw version of https://huggingface.co/wenbopan/Faro-Yi-9B-DPO |
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This is my first "quant" |
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Thanks to https://new.reddit.com/user/Downtown-Case-1755/ for making it known in https://new.reddit.com/r/LocalLLaMA/comments/1cziy0m/what_is_sota_for_a_mega_context_100k_novel/ that long-context models could be used by GPU-poor people who doesn't have 3090/4090. |
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# Faro-Yi-9B-DPO |
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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. On the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), it ranks **#2** among all 9B models, **#1** among all Yi-9B variants. |
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| **Metric** | **MMLU** | **GSM8K** | **hellaswag** | **truthfulqa** | **ai2_arc** | **winogrande** | **CMMLU** | |
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| ----------------------- | --------- | --------- | ------------- | -------------- | ----------- | -------------- | --------- | |
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| **Yi-9B-200K** | 65.73 | 50.49 | 56.72 | 33.80 | 69.25 | 71.67 | 71.97 | |
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| **Faro-Yi-9B** | 68.80 | 63.08 | 57.28 | 40.86 | 72.58 | 71.11 | 73.28 | |
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| **Faro-Yi-9B-DPO** | **69.98** | **66.11** | **59.04** | **48.01** | **75.68** | **73.40** | **75.23** | |
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Faro-Yi-9B-DPO's responses are also favored by GPT-4 Judge in MT-Bench |
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## How to Use |
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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. |
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```python |
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import io |
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import requests |
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from PyPDF2 import PdfReader |
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from vllm import LLM, SamplingParams |
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llm = LLM(model="wenbopan/Faro-Yi-9B-DPO", kv_cache_dtype="fp8_e5m2", max_model_len=100000) |
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pdf_data = io.BytesIO(requests.get("https://arxiv.org/pdf/2303.08774.pdf").content) |
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document = "".join(page.extract_text() for page in PdfReader(pdf_data).pages) # 100 pages |
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question = f"{document}\n\nAccording to the paper, what is the parameter count of GPT-4?" |
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messages = [ {"role": "user", "content": question} ] # 83K tokens |
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prompt = llm.get_tokenizer().apply_chat_template(messages, add_generation_prompt=True, tokenize=False) |
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output = llm.generate(prompt, SamplingParams(temperature=0.8, max_tokens=500)) |
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print(output[0].outputs[0].text) |
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# Yi-9B-200K: 175B. GPT-4 has 175B \nparameters. How many models were combined to create GPT-4? Answer: 6. ... |
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# 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. ... |
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``` |
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<details> <summary>Or With Transformers</summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained('wenbopan/Faro-Yi-9B-DPO', device_map="cuda") |
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tokenizer = AutoTokenizer.from_pretrained('wenbopan/Faro-Yi-9B-DPO') |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant. Always answer with a short response."}, |
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{"role": "user", "content": "Tell me what is Pythagorean theorem like you are a pirate."} |
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] |
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input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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generated_ids = model.generate(input_ids, max_new_tokens=512, temperature=0.5) |
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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. ... |
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``` |
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</details> |
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