Upload README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: question-answering
+---
+# Llama-mt-lora
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is fine-tuned with LLaMA with 8 Nvidia A100-80G GPUs using 3,000,000 groups of conversations in the context of mathematics by students and facilitators on Algebra Nation (https://www.mathnation.com/). Llama-mt-lora consists of 32 layers and over 7 billion parameters, consuming up to 13.5 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively respond generation in a mathematical context.
+### Here is how to use it with texts in HuggingFace
+```python
+import torch
+import transformers
+from transformers import LlamaTokenizer, AutoModelForCausalLM
+tokenizer = LlamaTokenizer.from_pretrained("Fan21/Llama-mt-lora")
+mdoel = LlamaForCausalLM.from_pretrained(
+        "Fan21/Llama-mt-lora",
+        load_in_8bit=False,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{input}
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Response:"""
+
+def evaluate(
+    instruction,
+    input=None,
+    temperature=0.1,
+    top_p=0.75,
+    top_k=40,
+    num_beams=4,
+    max_new_tokens=128,
+    **kwargs,
+):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(device)
+    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,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].strip()
+instruction = 'write your instruction here'
+inputs = 'write your inputs here'
+output= evaluate(instruction,
+                 input=inputs,
+                 temperature=0.1,#change the parameters by yourself
+                 top_p=0.75,
+                 top_k=40,
+                 num_beams=4,
+                  max_new_tokens=128,)
+```