114 lines
3.1 KiB
Markdown
114 lines
3.1 KiB
Markdown
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---
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tags:
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- generated_from_trainer
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- code
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- coding
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model-index:
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- name: FalCoder
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results: []
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license: apache-2.0
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language:
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- code
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thumbnail: https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png
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datasets:
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- HuggingFaceH4/CodeAlpaca_20K
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pipeline_tag: text-generation
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---
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<div style="text-align:center;width:250px;height:250px;">
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<img src="https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png" alt="falcoder logo"">
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</div>
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# FalCoder 🦅👩💻
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**Falcon-7b** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
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## Model description 🧠
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[Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
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## Training and evaluation data 📚
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[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model.
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### Training hyperparameters ⚙
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TBA
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### Training results 🗒️
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| Step | Training Loss | Validation Loss |
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|------|---------------|-----------------|
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| 100 | 0.798500 | 0.767996 |
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| 200 | 0.725900 | 0.749880 |
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| 300 | 0.669100 | 0.748029 |
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| 400 | 0.687300 | 0.742342 |
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| 500 | 0.579900 | 0.736735 |
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### Example of usage 👩💻
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer
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model_id = "mrm8488/falcoder-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
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def generate(
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instruction,
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max_new_tokens=128,
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temperature=0.1,
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top_p=0.75,
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top_k=40,
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num_beams=4,
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**kwargs
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):
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prompt = instruction + "\n### Solution:\n"
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print(prompt)
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inputs = tokenizer(prompt, return_tensors="pt")
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input_ids = inputs["input_ids"].to("cuda")
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attention_mask = inputs["attention_mask"].to("cuda")
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generation_config = GenerationConfig(
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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num_beams=num_beams,
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**kwargs,
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)
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with torch.no_grad():
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generation_output = model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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generation_config=generation_config,
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return_dict_in_generate=True,
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output_scores=True,
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max_new_tokens=max_new_tokens,
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early_stopping=True
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)
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s = generation_output.sequences[0]
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output = tokenizer.decode(s)
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return output.split("### Solution:")[1].lstrip("\n")
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instruction = "Design a class for representing a person in Python."
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print(generate(instruction))
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```
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### Citation
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```
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@misc {manuel_romero_2023,
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author = { {Manuel Romero} },
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title = { falcoder-7b (Revision e061237) },
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year = 2023,
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url = { https://huggingface.co/mrm8488/falcoder-7b },
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doi = { 10.57967/hf/0789 },
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publisher = { Hugging Face }
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}
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```
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