Create README.md

README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+
+base_model: meta-llama/Meta-Llama-3-8B
+base_model_relation: finetune
+---
+# Model Information
+The Llama-3-8B_SFT_Finetune_Pandas_Code is a quantized, fine-tuned version of the Meta-Llama-3 model designed specifically for analyzing tabular data.
+
+
+# How to use
+Starting with transformers version 4.34.0 and later, you can run conversational inference using the Transformers pipeline.
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+```python
+import transformers
+import torch
+from peft import PeftModel, PeftConfig, get_peft_model
+from transformers import pipeline
+```
+
+```python
+def get_pipline():
+    model_name = "nirusanan/Llama-3-8B_SFT_Finetune_Pandas_Code"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="cuda:0",
+        trust_remote_code=True
+    )
+
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=850)
+
+    return pipe
+
+pipe = get_pipline()
+```
+
+```python
+def generate_prompt(task, header_columns):
+    prompt = f"""Below is an instruction that describes a task. Write a Python function using Pandas to accomplish the task described below.
+
+### Instruction:
+{task}
+
+header columns with sample data:
+{header_columns}
+
+### Response:
+"""
+    return prompt
+```
+
+```python
+prompt = generate_prompt("Your question based on tabular data", "Necessary columns names")
+result = pipe(prompt)
+generated_text = result[0]['generated_text']
+print(generated_text.split("### End")[0])
+```