Update README.md

README.md

@@ -9,26 +9,34 @@ tags:
 model-index:
 - name: asm2asm-deepseek-1.3b-500k-2ep-x86-O0-risc
   results: []
+datasets:
+- ahmedheakl/asm2asm_O0_500000_gnueabi_gcc
+metrics:
+- exact_match
+- accuracy
 ---
 
-<!-- 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. -->
+# CISC-to-RISC
 
-# asm2asm-deepseek-1.3b-500k-2ep-x86-O0-risc
+A fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct) specialized in converting x86 assembly code to RISCv5-64 assembly.
 
-This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct) on an unknown dataset.
+## Model Overview
 
-## Model description
+**asm2asm-deepseek1.3b-xtokenizer-risc** is designed to assist developers in converting x86 assembly instructions to RISCv5-64 assembly. Leveraging the capabilities of the base model, this fine-tuned variant enhances accuracy and efficiency in assembly code transpilation tasks.
 
-More information needed
+## Intended Use
 
-## Intended uses & limitations
+This model is intended for:
 
-More information needed
+- **Assembly Code Conversion**: Assisting developers in translating x86 assembly instructions to RISCv5-64 architecture.
+- **Educational Purposes**: Helping learners understand the differences and translation mechanisms between x86 and RISCv5-64 assembly.
+- **Code Optimization**: Facilitating optimization processes by converting and refining assembly code across architectures.
 
-## Training and evaluation data
+## Limitations
 
-More information needed
+- **Dataset Specificity**: The model is fine-tuned on a specific dataset, which may limit its performance on assembly instructions outside the training distribution.
+- **Complex Instructions**: May struggle with highly complex or unconventional assembly instructions not well-represented in the training data.
+- **Error Propagation**: Inaccuracies in the generated RISCv5-64 code can lead to functional discrepancies or bugs if not reviewed.
 
 ## Training procedure
 
@@ -45,7 +53,99 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 2
 
-### Training results
+## Usage
+
+All models and datasets are available on [Hugging Face](https://huggingface.co/collections/ahmedheakl/cisc-to-risc-672727bd996db985473d146e). Below is an example of how to use the best model for converting x86 assembly to RISCv5-64.
+
+### Inference Code
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from tqdm import tqdm
+
+# Replace 'hf_token' with your Hugging Face token
+hf_token = "your_hf_token_here"
+
+model_name = "ahmedheakl/asm2asm-deepseek1.3b-risc"
+
+instruction = """<｜begin▁of▁sentence｜>You are a helpful coding assistant assistant on converting from x86 to RISCv64 assembly.
+### Instruction:
+Convert this x86 assembly into RISCv64
+```asm
+{asm_x86}
+"```"
+### Response:
+```asm
+{asm_risc}
+"""
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    token=hf_token,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+model.config.use_cache = True
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    token=hf_token,
+)
+
+def inference(asm_x86: str) -> str:
+    prompt = instruction.format(asm_x86=asm_x86, asm_risc="")
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    generated_ids = model.generate(
+        **inputs,
+        use_cache=True,
+        num_return_sequences=1,
+        max_new_tokens=8000,
+        do_sample=False,
+        num_beams=8,
+        # temperature=0.7,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+    outputs = tokenizer.batch_decode(generated_ids)[0]
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    return outputs.split("```asm\n")[-1].split(f"```{tokenizer.eos_token}")[0]
+
+
+x86 = "DWORD PTR -248[rbp] movsx rdx"
+converted_risc = inference(x86)
+print(converted_risc)
+```
+
+## Experiments and Results
+
+| **Model**                                     | **Average Edit Distance** (↓) | **Exact Match** (↑) | **Test Accuracy** (↑) |
+|-----------------------------------------------|-------------------------------|---------------------|-----------------------|
+| GPT4o                                         | 1296                          | 0%                  | 8.18%                 |
+| DeepSeekCoder2-16B                            | 1633                          | 0%                  | 7.36%                 |
+| Yi-Coder-9B                                   | 1653                          | 0%                  | 6.33%                 |
+| **Yi-Coder-1.5B**                             | 275                           | 16.98%              | 49.69%                |
+| **DeepSeekCoder-1.3B**                        | 107                           | 45.91%              | 77.23%                |
+| **DeepSeekCoder-1.3B-xTokenizer-int4**        | 119                           | 46.54%              | 72.96%                |
+| **DeepSeekCoder-1.3B-xTokenizer-int8**        | **96**                        | 49.69%              | 75.47%                |
+| **DeepSeekCoder-1.3B-xTokenizer**             | 165                           | **50.32%**          | **79.25%**            |
+
+*Table: Comparison of models' performance on the x86 to ARM transpilation task, measured by Edit Distance (lower is better), Exact Match (higher is better), and Test Accuracy (higher is better). The top section lists pre-existing models, while the bottom section lists models trained by us. The best results in each metric are highlighted in bold.*
+
+
+| **Model**                              | **Average Edit Distance** (↓) | **Exact Match** (↑) | **Test Accuracy** (↑) |
+|----------------------------------------|-------------------------------|---------------------|-----------------------|
+| GPT4o                                  | 1293                          | 0%                  | 7.55%                |
+| DeepSeekCoder2-16B                     | 1483                          | 0%                  | 6.29%                |
+|----------------------------------------|-------------------------------|---------------------|-----------------------|
+| DeepSeekCoder-1.3B-xTokenizer-int4     | 112                           | 14.47%              | 68.55%               |
+| DeepSeekCoder-1.3B-xTokenizer-int8     | 31                            | 69.81%              | 88.05%               |
+| DeepSeekCoder-1.3B-xTokenizer          | **27**                        | **69.81%**          | **88.68%**           |
+
+**Table:** Comparison of models' performance on the _x86 to RISCv64_ transpilation task.