Update README.md

README.md

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-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+### Introduction of Falcon3-decompile-3b
+
+Falcon3-decompiler-3b aims to decompile x86 assembly instructions into C.
+### Evaluation Results
+
+The benchmark that have been used is HumanEval benchmark from LLM4Decompile
+![](./falcon3.png)
+
+### How to Use
+
+Here is an example of how to use our model Note: Replace asm_func with the function that you want to decompile
+
+Decompilation: Use falcon3-decompiler-3b to translate ghidra decompilation output to more readable code:
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_path = 'LLM4Binary/llm4decompile-1.3b-v1.5' # V1.5 Model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16).cuda()
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+
+asm_func = """
+char * func0(char **param_1,int param_2)
+
+{
+  char **ppcVar1;
+  char *__s;
+  size_t sVar2;
+  int iVar3;
+  char *pcVar4;
+  
+  pcVar4 = "";
+  if (0 < param_2) {
+    iVar3 = 0;
+    ppcVar1 = param_1 + (ulong)(param_2 - 1) + 1;
+    do {
+      __s = *param_1;
+      sVar2 = strlen(__s);
+      if (iVar3 < (int)sVar2) {
+        pcVar4 = __s;
+        iVar3 = (int)sVar2;
+      }
+      param_1 = param_1 + 1;
+    } while (param_1 != ppcVar1);
+  }
+  return pcVar4;
+}
+"""
+
+before = f"# This is the assembly code:\n"#prompt
+after = "\n# What is the source code?\n"#prompt
+asm_func = before+asm_func.strip()+after
+model_path = "Neo111x/falcon3-decompiler-3b"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto").to("cuda:0")
+
+inputs = tokenizer(asm_func, return_tensors="pt").to("cuda:0")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
+c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
+
+# Note only decompile one function, where the original file may contain multiple functions
+
+print(f'decompiled function:\n{c_func_decompile}')
+```
+### Contact
+
+If you have any questions, please raise an issue.