Upload quantized model GLM-OCR-autoround-W4A16
Browse files- README.md +118 -0
- chat_template.jinja +140 -0
- config.json +81 -0
- generation_config.json +10 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +746 -0
- model_extra_tensors.safetensors +3 -0
- preprocessor_config.json +26 -0
- processor_config.json +63 -0
- quantization_config.json +11 -0
- tokenizer.json +0 -0
- tokenizer_config.json +50 -0
README.md
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---
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base_model:
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- zai-org/GLM-OCR
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pipeline_tag: text-generation
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tags:
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- quantized
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- w4a16
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- autoround
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- low-bit-open-llm-leaderboard
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---
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# GLM-OCR-autoround-W4A16
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## Model Details
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This model is a int4 weight-only quantization with group_size 128 and symmetric quantization of [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) generated by [AutoRound](https://github.com/intel/auto-round). Please follow the license of the original model.
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## Quantization Details
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| Attribute | Value |
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|-----------|-------|
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| Base Model | [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) |
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| Quantization Tool | [AutoRound](https://github.com/intel/auto-round) |
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| Quantization Scheme | W4A16 |
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| Original Size | 2528 MB |
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| Quantized Size | 1527 MB |
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## Evaluation Results
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| Task | Accuracy |
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|------|----------|
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| hellaswag | 0.2451 |
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| mmlu | 0.0000 |
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| piqa | 0.5033 |
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## How to Use
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### HF Usage
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**Step 1: Install [AutoRound](https://github.com/intel/auto-round)**
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```bash
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pip install auto-round
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```
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**Step 2: Load and run the quantized model**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "GLM-OCR-autoround-W4A16"
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# load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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# prepare the model input
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prompt = "Write a quick sort algorithm."
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messages = [{"role": "user", "content": prompt}]
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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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# conduct text completion
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generated_ids = model.generate(**model_inputs, max_new_tokens=512)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()
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content = tokenizer.decode(output_ids, skip_special_tokens=True)
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print("content:", content)
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```
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### VLLM Usage
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```bash
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vllm serve GLM-OCR-autoround-W4A16 \
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--trust-remote-code \
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--dtype bfloat16 \
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--tensor_parallel_size 1
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```
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If you encounter any issues, feel free to open an issue on the [AutoRound GitHub repo](https://github.com/intel/auto-round/issues) or provide feedback on the [Low-Bit Open LLM Leaderboard](https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard).
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## Ethical Considerations and Limitations
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The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
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Therefore, before deploying any applications of the model, developers should perform safety testing.
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## Caveats and Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
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Here are a couple of useful links to learn more about Intel's AI software:
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- [Intel Neural Compressor](https://github.com/intel/neural-compressor)
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- [AutoRound](https://github.com/intel/auto-round)
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## Disclaimer
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
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## Cite
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```
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@article{cheng2023optimize,
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title={Optimize weight rounding via signed gradient descent for the quantization of llms},
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author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
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journal={arXiv preprint arXiv:2309.05516},
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year={2023}
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}
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```
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[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
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---
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*This model is part of the [Intel Low-Bit Open LLM Leaderboard](https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard) initiative.*
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chat_template.jinja
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[gMASK]<sop>
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{%- if tools -%}
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<|system|>
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{% for tool in tools %}
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{{ tool | tojson(ensure_ascii=False) }}
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{% endfor %}
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</tools>
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For each function call, output the function name and arguments within the following XML format:
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<tool_call>{function-name}
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<arg_key>{arg-key-1}</arg_key>
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<arg_value>{arg-value-1}</arg_value>
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<arg_key>{arg-key-2}</arg_key>
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<arg_value>{arg-value-2}</arg_value>
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...
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</tool_call>{%- endif -%}
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{%- macro visible_text(content) -%}
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{%- if content is string -%}
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{{- content }}
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{%- elif content is iterable and content is not mapping -%}
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{%- for item in content -%}
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{%- if item is mapping and item.type == 'text' -%}
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{{- item.text }}
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{%- elif item is mapping and (item.type == 'image' or 'image' in item) -%}
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<|begin_of_image|><|image|><|end_of_image|>
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{%- elif item is mapping and (item.type == 'video' or 'video' in item) -%}
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<|begin_of_video|><|video|><|end_of_video|>
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{%- elif item is string -%}
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{{- item }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{- content }}
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{%- endif -%}
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{%- endmacro -%}
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{%- set ns = namespace(last_user_index=-1) %}
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{%- for m in messages %}
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{%- if m.role == 'user' %}
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{% set ns.last_user_index = loop.index0 -%}
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{%- endif %}
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{%- endfor %}
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{% for m in messages %}
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{%- if m.role == 'user' -%}<|user|>
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{% if m.content is string %}
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{{ m.content }}
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{%- else %}
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{%- for item in m.content %}
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{% if item.type == 'video' or 'video' in item %}
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<|begin_of_video|><|video|><|end_of_video|>{% elif item.type == 'image' or 'image' in item %}
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<|begin_of_image|><|image|><|end_of_image|>{% elif item.type == 'text' %}
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{{ item.text }}
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| 58 |
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{%- endif %}
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| 59 |
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{%- endfor %}
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| 60 |
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{%- endif %}
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{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}
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{%- elif m.role == 'assistant' -%}
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<|assistant|>
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{%- set reasoning_content = '' %}
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{%- set content = visible_text(m.content) %}
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{%- if m.reasoning_content is string %}
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{%- set reasoning_content = m.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_user_index and reasoning_content -%}
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{{ '\n<think>' + reasoning_content.strip() + '</think>'}}
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{%- else -%}
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| 77 |
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{{ '\n<think></think>' }}
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| 78 |
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{%- endif -%}
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| 79 |
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{%- if content.strip() -%}
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| 80 |
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{{ '\n' + content.strip() }}
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| 81 |
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{%- endif -%}
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{% if m.tool_calls %}
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{% for tc in m.tool_calls %}
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{%- if tc.function %}
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{%- set tc = tc.function %}
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| 86 |
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{%- endif %}
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| 87 |
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{{ '\n<tool_call>' + tc.name }}
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{% set _args = tc.arguments %}
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| 89 |
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{% for k, v in _args.items() %}
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| 90 |
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<arg_key>{{ k }}</arg_key>
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| 91 |
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<arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>
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| 92 |
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{% endfor %}
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| 93 |
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</tool_call>{% endfor %}
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| 94 |
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{% endif %}
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| 95 |
+
{%- elif m.role == 'tool' -%}
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| 96 |
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{%- if m.content is string -%}
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| 97 |
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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| 98 |
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{{- '<|observation|>' }}
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| 99 |
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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| 101 |
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{{- m.content }}
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| 102 |
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{{- '\n</tool_response>' }}
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| 103 |
+
{% elif m.content is iterable and m.content is not mapping %}
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| 104 |
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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| 105 |
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{{- '<|observation|>' }}
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| 106 |
+
{%- endif %}
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| 107 |
+
{{- '\n<tool_response>\n' }}
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| 108 |
+
{%- for tr in m.content -%}
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| 109 |
+
{%- if tr is mapping and tr.type is defined -%}
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| 110 |
+
{%- set t = tr.type | lower -%}
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| 111 |
+
{%- if t == 'text' and tr.text is defined -%}
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| 112 |
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{{ tr.text }}
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| 113 |
+
{%- elif t in ['image', 'image_url'] -%}
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| 114 |
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<|begin_of_image|><|image|><|end_of_image|>
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| 115 |
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{%- elif t in ['video', 'video_url'] -%}
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| 116 |
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<|begin_of_video|><|video|><|end_of_video|>
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| 117 |
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{%- else -%}
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| 118 |
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{{ tr | tojson(ensure_ascii=False) }}
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| 119 |
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{%- endif -%}
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| 120 |
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{%- else -%}
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| 121 |
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{{ tr.output if tr.output is defined else tr }}
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| 122 |
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{%- endif -%}
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| 123 |
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{%- endfor -%}
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| 124 |
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{{- '\n</tool_response>' }}
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| 125 |
+
{%- else -%}
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| 126 |
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<|observation|>{% for tr in m.content %}
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| 127 |
+
|
| 128 |
+
<tool_response>
|
| 129 |
+
{{ tr.output if tr.output is defined else tr }}
|
| 130 |
+
</tool_response>{% endfor -%}
|
| 131 |
+
{% endif -%}
|
| 132 |
+
{%- elif m.role == 'system' -%}
|
| 133 |
+
<|system|>
|
| 134 |
+
{{ visible_text(m.content) }}
|
| 135 |
+
{%- endif -%}
|
| 136 |
+
{%- endfor -%}
|
| 137 |
+
{%- if add_generation_prompt -%}
|
| 138 |
+
<|assistant|>
|
| 139 |
+
{{'<think></think>\n' if (enable_thinking is defined and not enable_thinking) else ''}}
|
| 140 |
+
{%- endif -%}
|
config.json
ADDED
|
@@ -0,0 +1,81 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"GlmOcrForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"dtype": "bfloat16",
|
| 6 |
+
"image_end_token_id": 59257,
|
| 7 |
+
"image_start_token_id": 59256,
|
| 8 |
+
"image_token_id": 59280,
|
| 9 |
+
"model_type": "glm_ocr",
|
| 10 |
+
"quantization_config": {
|
| 11 |
+
"autoround_version": "0.12.3",
|
| 12 |
+
"bits": 4,
|
| 13 |
+
"block_name_to_quantize": "model.language_model.layers",
|
| 14 |
+
"data_type": "int",
|
| 15 |
+
"group_size": 128,
|
| 16 |
+
"iters": 0,
|
| 17 |
+
"packing_format": "auto_round:auto_gptq",
|
| 18 |
+
"quant_method": "auto-round",
|
| 19 |
+
"sym": true
|
| 20 |
+
},
|
| 21 |
+
"text_config": {
|
| 22 |
+
"attention_bias": false,
|
| 23 |
+
"attention_dropout": 0.0,
|
| 24 |
+
"dtype": "bfloat16",
|
| 25 |
+
"eos_token_id": [
|
| 26 |
+
59246,
|
| 27 |
+
59253
|
| 28 |
+
],
|
| 29 |
+
"head_dim": 128,
|
| 30 |
+
"hidden_act": "silu",
|
| 31 |
+
"hidden_size": 1536,
|
| 32 |
+
"initializer_range": 0.02,
|
| 33 |
+
"intermediate_size": 4608,
|
| 34 |
+
"max_position_embeddings": 131072,
|
| 35 |
+
"model_type": "glm_ocr_text",
|
| 36 |
+
"num_attention_heads": 16,
|
| 37 |
+
"num_hidden_layers": 16,
|
| 38 |
+
"num_key_value_heads": 8,
|
| 39 |
+
"num_nextn_predict_layers": 1,
|
| 40 |
+
"pad_token_id": 59246,
|
| 41 |
+
"rms_norm_eps": 1e-05,
|
| 42 |
+
"rope_parameters": {
|
| 43 |
+
"mrope_section": [
|
| 44 |
+
16,
|
| 45 |
+
24,
|
| 46 |
+
24
|
| 47 |
+
],
|
| 48 |
+
"partial_rotary_factor": 1.0,
|
| 49 |
+
"rope_theta": 10000,
|
| 50 |
+
"rope_type": "default"
|
| 51 |
+
},
|
| 52 |
+
"tie_word_embeddings": false,
|
| 53 |
+
"use_cache": true,
|
| 54 |
+
"vocab_size": 59392
|
| 55 |
+
},
|
| 56 |
+
"tie_word_embeddings": false,
|
| 57 |
+
"transformers_version": "5.9.0",
|
| 58 |
+
"video_end_token_id": 59259,
|
| 59 |
+
"video_start_token_id": 59258,
|
| 60 |
+
"video_token_id": 59281,
|
| 61 |
+
"vision_config": {
|
| 62 |
+
"attention_bias": true,
|
| 63 |
+
"attention_dropout": 0.0,
|
| 64 |
+
"depth": 24,
|
| 65 |
+
"dtype": "bfloat16",
|
| 66 |
+
"hidden_act": "silu",
|
| 67 |
+
"hidden_dropout_prob": 0.0,
|
| 68 |
+
"hidden_size": 1024,
|
| 69 |
+
"image_size": 336,
|
| 70 |
+
"in_channels": 3,
|
| 71 |
+
"initializer_range": 0.02,
|
| 72 |
+
"intermediate_size": 4096,
|
| 73 |
+
"model_type": "glm_ocr_vision",
|
| 74 |
+
"num_heads": 16,
|
| 75 |
+
"out_hidden_size": 1536,
|
| 76 |
+
"patch_size": 14,
|
| 77 |
+
"rms_norm_eps": 1e-05,
|
| 78 |
+
"spatial_merge_size": 2,
|
| 79 |
+
"temporal_patch_size": 2
|
| 80 |
+
}
|
| 81 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"do_sample": false,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
59246,
|
| 6 |
+
59253
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 59246,
|
| 9 |
+
"transformers_version": "5.9.0"
|
| 10 |
+
}
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bb3dd74b8c4af6978e1bf32f4d34afe8c1c915a57824ab646e882890496006bf
|
| 3 |
+
size 1068211520
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4cb74129840156596f7dc93de5b785a097a79cb4608ba25edb1dd44251ec59e3
|
| 3 |
+
size 420168760
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,746 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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preprocessor_config.json
ADDED
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{
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|
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processor_config.json
ADDED
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@@ -0,0 +1,63 @@
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{
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|
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|
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|
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|
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"video_processor_type": "Glm46VVideoProcessor"
|
| 62 |
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|
| 63 |
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|
quantization_config.json
ADDED
|
@@ -0,0 +1,11 @@
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|
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|
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|
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,50 @@
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|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"clean_up_tokenization_spaces": false,
|
| 4 |
+
"eos_token": "<|endoftext|>",
|
| 5 |
+
"extra_special_tokens": [
|
| 6 |
+
"<|endoftext|>",
|
| 7 |
+
"[MASK]",
|
| 8 |
+
"[gMASK]",
|
| 9 |
+
"[sMASK]",
|
| 10 |
+
"<sop>",
|
| 11 |
+
"<eop>",
|
| 12 |
+
"<|system|>",
|
| 13 |
+
"<|user|>",
|
| 14 |
+
"<|assistant|>",
|
| 15 |
+
"<|observation|>",
|
| 16 |
+
"<|begin_of_image|>",
|
| 17 |
+
"<|end_of_image|>",
|
| 18 |
+
"<|begin_of_video|>",
|
| 19 |
+
"<|end_of_video|>",
|
| 20 |
+
"<|begin_of_audio|>",
|
| 21 |
+
"<|end_of_audio|>",
|
| 22 |
+
"<|begin_of_transcription|>",
|
| 23 |
+
"<|end_of_transcription|>",
|
| 24 |
+
"<|code_prefix|>",
|
| 25 |
+
"<|code_middle|>",
|
| 26 |
+
"<|code_suffix|>",
|
| 27 |
+
"<think>",
|
| 28 |
+
"</think>",
|
| 29 |
+
"<tool_call>",
|
| 30 |
+
"</tool_call>",
|
| 31 |
+
"<tool_response>",
|
| 32 |
+
"</tool_response>",
|
| 33 |
+
"<arg_key>",
|
| 34 |
+
"</arg_key>",
|
| 35 |
+
"<arg_value>",
|
| 36 |
+
"</arg_value>",
|
| 37 |
+
"/nothink",
|
| 38 |
+
"<|begin_of_box|>",
|
| 39 |
+
"<|end_of_box|>",
|
| 40 |
+
"<|image|>",
|
| 41 |
+
"<|video|>"
|
| 42 |
+
],
|
| 43 |
+
"is_local": false,
|
| 44 |
+
"local_files_only": false,
|
| 45 |
+
"model_max_length": 655380,
|
| 46 |
+
"pad_token": "<|endoftext|>",
|
| 47 |
+
"padding_side": "left",
|
| 48 |
+
"processor_class": "Glm46VProcessor",
|
| 49 |
+
"tokenizer_class": "TokenizersBackend"
|
| 50 |
+
}
|