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--- |
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tags: |
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- generated_from_trainer |
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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: >- |
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https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png |
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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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## Dataset |
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[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) |
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## Intended uses & limitations |
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TBA |
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## Training and evaluation data |
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TBA |
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### Training hyperparameters |
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TBA |
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### Training results |
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TBA |
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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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