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Model: XXXiong/ChatHLS-HLSTuner Source: Original Platform
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README.md
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README.md
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- chat
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- llm
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- safetensors
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base_model: Qwen/Qwen2.5-14B-Instruct
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language:
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- en
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---
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# ChatHLS-HLSTuner
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## Model Details
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- **Finetuned from model:** `Qwen/Qwen2.5-Coder-14B-Instruct`
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- **Model source paper:** [ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis](https://aclanthology.org/2026.acl-long.962/)
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- **Project repository:** [GEAR-SEU/ChatHLS-ACL-26](https://github.com/GEAR-SEU/ChatHLS-ACL-26)
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## Description
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**ChatHLS-HLSTuner** is a specialized Large Language Model fine-tuned for High-Level Synthesis (HLS) C/C++ code optimization. It acts as the optimization analysis agent within the ChatHLS framework.
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## Quickstart
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### Prerequisites
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```bash
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pip install transformers torch accelerate
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```
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### Example
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Here is a simple example of how to format your prompt and generate optimization analysis for HLS code:
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````python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "XXXiong/ChatHLS-HLSTuner"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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hls_code = """
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#include "ap_fixed.h"
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#include "hls_math.h"
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typedef ap_fixed<32,16> t_ap_fixed;
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void gemm(
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t_ap_fixed alpha,
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t_ap_fixed beta,
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t_ap_fixed C[ 20 + 0][25 + 0],
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t_ap_fixed A[ 20 + 0][30 + 0],
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t_ap_fixed B[ 30 + 0][25 + 0])
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{
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#pragma HLS top name=gemm
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const int ni = 20;
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const int nj = 25;
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const int nk = 30;
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int i, j, k;
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L1: for (i = 0; i < ni; i++) {
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L2: for (j = 0; j < nj; j++)
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C[i][j] *= beta;
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L3: for (k = 0; k < nk; k++) {
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L4: for (j = 0; j < nj; j++)
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C[i][j] += alpha * A[i][k] * B[k][j];
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}
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}
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}
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"""
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hls_code_ppa = """
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latency: 15661 cycles
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util_dsp: 10.5%
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util_ff: 0.4%
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util_lut: 2.1%
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"""
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loop_array_info = """
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kernel_gemm
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L1,loop,20
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L2,loop,25
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L3,loop,30
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L4,loop,25
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C,array,20/25
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A,array,20/30
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B,array,30/25
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"""
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prompt = f"""Your task is to optimize the following HLS kernel by inserting `#pragma` directives
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to enhance loops and arrays for lower latency and relatively low resource utilization.
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You may use the following pragma types:
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- Loop Pipeline
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- Loop Unroll
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- Array Partition
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Provided code:
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{hls_code}
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Current latency and hardware resource utilization:
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{hls_code_ppa}
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Loop and array information:
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{loop_array_info}
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Please suggest pragma modifications, specifying where to insert them and the reasoning behind each.
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Provide only the suggestions without additional explanations or formatting.
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**Expected Output Format**:
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Detailed Reasoning:
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```text
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[Your step-by-step analysis here]
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```
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Optimization Strategies:
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```json
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{{
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"Optimization Strategies": [
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{{
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"Code Segment Before Optimization (Loop/Array)": "[Code line before optimization]",
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"Code Segment After Optimization (Loop/Array)": "[Code line after inserting the pragma]",
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"Reason": "[Explanation of why this pragma was inserted]",
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"Result Analysis": {{
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"Impact of Inserting This Pragma on PPA": "[Description of the impact on Performance, Power, and Area]",
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"Reason": "[Explanation of why this impact occurred based on the performance analysis]"
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}}
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}}
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]
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}}
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```
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"""
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messages = [
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{"role": "system", "content": """You are an expert proficient in HLS algorithm optimization,
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improving algorithm PPA through inserting pragmas into arrays and loops in HLS code."""},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048,
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temperature=0.7
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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````
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