Create README.md

README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: PomeranIAn
+  results: []
+license: apache-2.0
+language:
+- code
+thumbnail: >-
+  https://huggingface.co/mrm8488/pomeranian/resolve/main/pomeranian-removebg-preview.png
+---
+
+<div style="text-align:center;width:250px;height:250px;">
+    <img src="https://huggingface.co/mrm8488/pomeranian/resolve/main/pomeranian-removebg-preview.png" alt="pomeranian logo"">
+</div>
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# FalCoder
+**Falcon-7b** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
+
+## Model description
+
+[Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
+
+## Dataset
+
+[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K)
+
+## Intended uses & limitations
+
+TBA
+
+## Training and evaluation data
+
+TBA
+
+### Training hyperparameters
+
+TBA
+
+### Training results
+
+TBA
+
+
+### Example of usage
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer
+
+model_id = "mrm8488/falcoder-7b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
+
+def generate(
+        instruction,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs
+):
+    prompt = instruction + "\n### Solution:\n"
+    print(prompt)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+            early_stopping=True
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Solution:")[1].lstrip("\n")
+
+instruction = "Design a class for representing a person in Python."
+print(generate(instruction))
+```
+