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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, GenerationConfig |
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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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``` |
