Training in progress, step 3
Browse files- README.md +9 -9
- adapter_config.json +3 -4
- adapter_model.safetensors +2 -2
- added_tokens.json +0 -8
- chat_template.json +3 -0
- generation_config.json +15 -0
- preprocessor_config.json +29 -0
- tokenizer.json +2 -2
- tokenizer_config.json +3 -67
- training_args.bin +1 -1
README.md
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---
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base_model: Qwen/Qwen2
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library_name: transformers
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model_name: qwen25-3b-instruct-trl-sft-qa-hr
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tags:
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# Model Card for qwen25-3b-instruct-trl-sft-qa-hr
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This model is a fine-tuned version of [Qwen/Qwen2
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chrisapratikaki/qwen25-
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.
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- Transformers: 4.
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- Pytorch: 2.
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- Datasets: 3.
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- Tokenizers: 0.21.
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## Citations
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---
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base_model: Qwen/Qwen2-VL-7B-Instruct
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library_name: transformers
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model_name: qwen25-3b-instruct-trl-sft-qa-hr
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tags:
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# Model Card for qwen25-3b-instruct-trl-sft-qa-hr
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+
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="cpratikaki/qwen25-3b-instruct-trl-sft-qa-hr", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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+
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chrisapratikaki/qwen25-7b-instruct-testset/runs/e37m06c1)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.47.0
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- Pytorch: 2.4.1+cu121
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- Datasets: 3.4.1
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- Tokenizers: 0.21.1
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## Citations
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adapter_config.json
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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added_tokens.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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generation_config.json
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preprocessor_config.json
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"image_processor_type": "Qwen2VLImageProcessor",
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"image_std": [
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"processor_class": "Qwen2_5_VLProcessor",
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tokenizer.json
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tokenizer_config.json
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|
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training_args.bin
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