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Model: XXXiong/ChatHLS-HLSTuner Source: Original Platform
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README.md
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---
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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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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
|
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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],
|
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"max_position_embeddings": 32768,
|
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"max_window_layers": 48,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 40,
|
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"num_hidden_layers": 48,
|
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"use_cache": true,
|
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"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
13
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|
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}
|
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||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user