Instructions to use kinit/hyperproof-solver-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kinit/hyperproof-solver-sft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kinit/hyperproof-solver-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- .gitattributes +1 -0
- README.md +58 -0
- adapter_config.json +55 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +20 -0
- tokenizer.json +3 -0
- tokenizer_config.json +32 -0
- training_args.bin +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: Qwen/Qwen3.5-9B
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library_name: transformers
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model_name: hyperproof-solver-sft
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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 hyperproof-solver-sft
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This model is a fine-tuned version of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B).
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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="kinit/hyperproof-solver-sft", 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/kinit-sk/llm-equational-prover/runs/y4tdefic)
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This model was trained with SFT.
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### Framework versions
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- TRL: 1.10.0
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- Transformers: 5.15.1
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- Pytorch: 2.11.0+cu130
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- Datasets: 5.0.1
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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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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "Qwen/Qwen3.5-9B",
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"bias": "none",
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| 8 |
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 256,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"monteclora_config": null,
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"peft_type": "LORA",
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"peft_version": "0.20.0",
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"qalora_group_size": 16,
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"r": 128,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"in_proj_a",
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"up_proj",
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"gate_proj",
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"o_proj",
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"in_proj_z",
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"v_proj",
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"down_proj",
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"in_proj_b",
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"q_proj",
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"out_proj",
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"in_proj_qkv",
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"k_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false,
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"velora_config": null
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7196a05434a33d93df0f37c80b26acc5c0c98fc254f9798c9942abd713dfe65d
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size 1384975816
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chat_template.jinja
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{%- if messages and messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set loop_messages = messages %}
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{%- endif %}
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{%- for message in loop_messages %}
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{{- '<|im_start|>' + message.role + '\n' }}
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| 9 |
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{%- if message.role == 'assistant' %}
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{% generation %}{{- message.content + '<|im_end|>' }}{% endgeneration %}{{- '\n' }}
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{%- else %}
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{{- message.content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endfor %}
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| 15 |
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{%- if add_generation_prompt %}
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| 16 |
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is not defined or not enable_thinking %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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size 19989325
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"audio_bos_token": "<|audio_start|>",
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"audio_eos_token": "<|audio_end|>",
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| 5 |
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"audio_token": "<|audio_pad|>",
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"backend": "tokenizers",
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| 7 |
+
"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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| 11 |
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"image_token": "<|image_pad|>",
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| 12 |
+
"is_local": false,
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| 13 |
+
"local_files_only": false,
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| 14 |
+
"model_max_length": 262144,
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| 15 |
+
"model_specific_special_tokens": {
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| 16 |
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"audio_bos_token": "<|audio_start|>",
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| 17 |
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"audio_eos_token": "<|audio_end|>",
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| 18 |
+
"audio_token": "<|audio_pad|>",
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| 19 |
+
"image_token": "<|image_pad|>",
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| 20 |
+
"video_token": "<|video_pad|>",
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| 21 |
+
"vision_bos_token": "<|vision_start|>",
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| 22 |
+
"vision_eos_token": "<|vision_end|>"
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| 23 |
+
},
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| 24 |
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"pad_token": "<|endoftext|>",
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| 25 |
+
"pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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| 26 |
+
"split_special_tokens": false,
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| 27 |
+
"tokenizer_class": "Qwen2Tokenizer",
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| 28 |
+
"unk_token": null,
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| 29 |
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"video_token": "<|video_pad|>",
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| 30 |
+
"vision_bos_token": "<|vision_start|>",
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| 31 |
+
"vision_eos_token": "<|vision_end|>"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:63584b641880ca8f7031099a802ca6e2de6c8e3fdacc7cdffeaa3f69e77b5a9a
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| 3 |
+
size 5969
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