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Model: dizza01/qwen2.5-7b-finetunerag-merged
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2026-05-06 22:59:16 +08:00
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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]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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### Recommendations
<!-- 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.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
<!-- 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
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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## Environmental Impact
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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).
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.4.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.4.0"
}

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import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class EndpointHandler:
def __init__(self, path: str = ""):
model_dir = path or "/repository"
self.tokenizer = AutoTokenizer.from_pretrained(
model_dir,
trust_remote_code=True,
)
# Ensure pad token exists for generation
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.model = AutoModelForCausalLM.from_pretrained(
model_dir,
trust_remote_code=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto",
)
self.model.eval()
def __call__(self, data):
inputs = data.get("inputs", "")
params = data.get("parameters", {}) or {}
max_new_tokens = int(params.get("max_new_tokens", 128))
temperature = float(params.get("temperature", 0.0))
top_p = float(params.get("top_p", 1.0))
do_sample = bool(params.get("do_sample", temperature > 0))
# Accept either plain string input or chat-style messages
if isinstance(inputs, list):
prompt = self.tokenizer.apply_chat_template(
inputs,
tokenize=False,
add_generation_prompt=True,
)
else:
prompt = str(inputs)
enc = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
with torch.no_grad():
out = self.model.generate(
**enc,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=do_sample,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
)
generated_ids = out[0][enc["input_ids"].shape[-1]:]
text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
return {"generated_text": text}

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version https://git-lfs.github.com/spec/v1
oid sha256:746f915214a0f31462e910a514b5bea0a5a4115d1be01c39a3230182ca881098
size 15231272152

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transformers>=4.51.3
torch>=2.2.0
accelerate>=0.34.0
safetensors>=0.4.0
sentencepiece>=0.2.0

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version https://git-lfs.github.com/spec/v1
oid sha256:2e0069c5cc46024587fffe11fda990002d5e28b9e20a762ff1bb4f2480ce17aa
size 11422172

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {
"<|im_start|>": "<|im_start|>",
"<|im_end|>": "<|im_end|>",
"<|object_ref_start|>": "<|object_ref_start|>",
"<|object_ref_end|>": "<|object_ref_end|>",
"<|box_start|>": "<|box_start|>",
"<|box_end|>": "<|box_end|>",
"<|quad_start|>": "<|quad_start|>",
"<|quad_end|>": "<|quad_end|>",
"<|vision_start|>": "<|vision_start|>",
"<|vision_end|>": "<|vision_end|>",
"<|vision_pad|>": "<|vision_pad|>",
"<|image_pad|>": "<|image_pad|>",
"<|video_pad|>": "<|video_pad|>"
},
"is_local": false,
"max_length": 2048,
"model_max_length": 131072,
"pad_to_multiple_of": null,
"pad_token": "<|endoftext|>",
"pad_token_type_id": 0,
"padding_side": "right",
"split_special_tokens": false,
"stride": 0,
"tokenizer_class": "Qwen2Tokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": null
}