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Model: anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic Source: Original Platform
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README.md
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README.md
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---
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base_model: anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic
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library_name: transformers
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model_name: OpenMath-Nemotron-1.5B-PruneAgnostic
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tags:
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- generated_from_trainer
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- sft
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- trl
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licence: license
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---
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# Model Card for OpenMath-Nemotron-1.5B-PruneAgnostic
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This model is a fine-tuned version of [anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic](https://huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic).
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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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```python
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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="anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic", 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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This model was trained with SFT.
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### Framework versions
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- TRL: 0.29.0
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- Transformers: 5.0.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.0.0
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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chat_template.jinja
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{%- if messages[0]['role'] == 'system' -%}
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<|im_start|>system
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{{ messages[0]['content'] | trim }}<|im_end|>
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{%- else -%}
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<|im_start|>system
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<|im_end|>
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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') -%}
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<|im_start|>{{ message.role }}
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{%- if message['role'] == 'assistant' %}
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{% generation %}{{ message['content'] | trim }}<|im_end|>{% endgeneration %}
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{%- else %}
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{{ message['content'] | trim }}<|im_end|>
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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
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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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"bos_token_id": null,
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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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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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],
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"max_position_embeddings": 131072,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"rope_parameters": {
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"rope_type": "default"
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"tie_word_embeddings": true,
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"use_cache": false,
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}
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generation_config.json
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tokenizer_config.json
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"add_prefix_space": false,
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>",
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"[THOUGHT]",
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"[SOLUTION]",
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"[RETURN]",
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"<think>",
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"</think>"
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],
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"is_local": false,
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