Instructions to use hypaai/hypa-tiny-keys-old with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hypaai/hypa-tiny-keys-old with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hypaai/hypa-tiny-keys-old")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hypaai/hypa-tiny-keys-old")
model = AutoModelForCausalLM.from_pretrained("hypaai/hypa-tiny-keys-old", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hypaai/hypa-tiny-keys-old with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hypaai/hypa-tiny-keys-old"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys-old",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hypaai/hypa-tiny-keys-old

SGLang

How to use hypaai/hypa-tiny-keys-old with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hypaai/hypa-tiny-keys-old" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys-old",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hypaai/hypa-tiny-keys-old" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys-old",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hypaai/hypa-tiny-keys-old with Docker Model Runner:
```
docker model run hf.co/hypaai/hypa-tiny-keys-old
```

hypa-tiny-keys-old / README.md

hypaai

Model save

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	---
	library_name: transformers
	tags:
	- generated_from_trainer
	model-index:
	- name: hypa-tiny-keys
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# hypa-tiny-keys

	This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1086

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0003
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 3407
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- training_steps: 60000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 2.8771 \| 0.0083 \| 500 \| 2.8239 \|
	\| 2.3237 \| 0.0167 \| 1000 \| 2.2951 \|
	\| 1.9399 \| 0.025 \| 1500 \| 1.9368 \|
	\| 1.7840 \| 0.0333 \| 2000 \| 1.7695 \|
	\| 1.6826 \| 0.0417 \| 2500 \| 1.6788 \|
	\| 1.6208 \| 0.05 \| 3000 \| 1.6156 \|
	\| 1.5774 \| 0.0583 \| 3500 \| 1.5753 \|
	\| 1.5343 \| 0.0667 \| 4000 \| 1.5389 \|
	\| 1.5108 \| 0.075 \| 4500 \| 1.5089 \|
	\| 1.4918 \| 0.0833 \| 5000 \| 1.4879 \|
	\| 1.4696 \| 0.0917 \| 5500 \| 1.4679 \|
	\| 1.4439 \| 0.1 \| 6000 \| 1.4490 \|
	\| 1.4353 \| 0.1083 \| 6500 \| 1.4347 \|
	\| 1.4301 \| 0.1167 \| 7000 \| 1.4197 \|
	\| 1.4114 \| 0.125 \| 7500 \| 1.4098 \|
	\| 1.3965 \| 0.1333 \| 8000 \| 1.3979 \|
	\| 1.3879 \| 0.1417 \| 8500 \| 1.3883 \|
	\| 1.3754 \| 0.15 \| 9000 \| 1.3759 \|
	\| 1.3746 \| 0.1583 \| 9500 \| 1.3687 \|
	\| 1.3556 \| 0.1667 \| 10000 \| 1.3607 \|
	\| 1.3897 \| 0.175 \| 10500 \| 1.3605 \|
	\| 1.3735 \| 0.1833 \| 11000 \| 1.3588 \|
	\| 1.3561 \| 0.1917 \| 11500 \| 1.3561 \|
	\| 1.3514 \| 0.2 \| 12000 \| 1.3529 \|
	\| 1.3370 \| 0.2083 \| 12500 \| 1.3490 \|
	\| 1.3319 \| 0.2167 \| 13000 \| 1.3470 \|
	\| 1.3339 \| 0.225 \| 13500 \| 1.3440 \|
	\| 1.3161 \| 0.2333 \| 14000 \| 1.3415 \|
	\| 1.3142 \| 0.2417 \| 14500 \| 1.3338 \|
	\| 1.3061 \| 0.25 \| 15000 \| 1.3280 \|
	\| 1.2954 \| 0.2583 \| 15500 \| 1.3279 \|
	\| 1.2961 \| 0.2667 \| 16000 \| 1.3253 \|
	\| 1.2917 \| 0.275 \| 16500 \| 1.3156 \|
	\| 1.2850 \| 0.2833 \| 17000 \| 1.3168 \|
	\| 1.2734 \| 0.2917 \| 17500 \| 1.3135 \|
	\| 1.2637 \| 0.3 \| 18000 \| 1.3080 \|
	\| 1.2708 \| 0.3083 \| 18500 \| 1.3039 \|
	\| 1.2625 \| 0.3167 \| 19000 \| 1.3002 \|
	\| 1.2623 \| 0.325 \| 19500 \| 1.2992 \|
	\| 1.2482 \| 0.3333 \| 20000 \| 1.2961 \|
	\| 1.2453 \| 0.3417 \| 20500 \| 1.2909 \|
	\| 1.2873 \| 0.35 \| 21000 \| 1.2822 \|
	\| 1.2766 \| 0.3583 \| 21500 \| 1.2847 \|
	\| 1.2673 \| 0.3667 \| 22000 \| 1.2796 \|
	\| 1.2618 \| 0.375 \| 22500 \| 1.2755 \|
	\| 1.2543 \| 0.3833 \| 23000 \| 1.2704 \|
	\| 1.2471 \| 0.3917 \| 23500 \| 1.2728 \|
	\| 1.2345 \| 0.4 \| 24000 \| 1.2703 \|
	\| 1.2350 \| 0.4083 \| 24500 \| 1.2690 \|
	\| 1.2337 \| 0.4167 \| 25000 \| 1.2657 \|
	\| 1.2304 \| 0.425 \| 25500 \| 1.2619 \|
	\| 1.2202 \| 0.4333 \| 26000 \| 1.2560 \|
	\| 1.2182 \| 0.4417 \| 26500 \| 1.2521 \|
	\| 1.2137 \| 0.45 \| 27000 \| 1.2488 \|
	\| 1.2118 \| 0.4583 \| 27500 \| 1.2478 \|
	\| 1.2024 \| 0.4667 \| 28000 \| 1.2449 \|
	\| 1.1989 \| 0.475 \| 28500 \| 1.2446 \|
	\| 1.1929 \| 0.4833 \| 29000 \| 1.2421 \|
	\| 1.1942 \| 0.4917 \| 29500 \| 1.2386 \|
	\| 1.1842 \| 0.5 \| 30000 \| 1.2354 \|
	\| 1.1798 \| 0.5083 \| 30500 \| 1.2325 \|
	\| 1.2264 \| 0.5167 \| 31000 \| 1.2270 \|
	\| 1.2130 \| 0.525 \| 31500 \| 1.2215 \|
	\| 1.2058 \| 0.5333 \| 32000 \| 1.2180 \|
	\| 1.1970 \| 0.5417 \| 32500 \| 1.2136 \|
	\| 1.1886 \| 0.55 \| 33000 \| 1.2118 \|
	\| 1.1826 \| 0.5583 \| 33500 \| 1.2106 \|
	\| 1.1798 \| 0.5667 \| 34000 \| 1.2090 \|
	\| 1.1714 \| 0.575 \| 34500 \| 1.2061 \|
	\| 1.1707 \| 0.5833 \| 35000 \| 1.2036 \|
	\| 1.1774 \| 0.5917 \| 35500 \| 1.2013 \|
	\| 1.1602 \| 0.6 \| 36000 \| 1.1985 \|
	\| 1.1625 \| 0.6083 \| 36500 \| 1.1928 \|
	\| 1.1514 \| 0.6167 \| 37000 \| 1.1921 \|
	\| 1.1507 \| 0.625 \| 37500 \| 1.1902 \|
	\| 1.1444 \| 0.6333 \| 38000 \| 1.1878 \|
	\| 1.1425 \| 0.6417 \| 38500 \| 1.1865 \|
	\| 1.1393 \| 0.65 \| 39000 \| 1.1819 \|
	\| 1.1355 \| 0.6583 \| 39500 \| 1.1793 \|
	\| 1.1277 \| 0.6667 \| 40000 \| 1.1765 \|
	\| 1.1314 \| 0.675 \| 40500 \| 1.1780 \|
	\| 1.1272 \| 0.6833 \| 41000 \| 1.1748 \|
	\| 1.1607 \| 0.6917 \| 41500 \| 1.1643 \|
	\| 1.1553 \| 0.7 \| 42000 \| 1.1613 \|
	\| 1.1484 \| 0.7083 \| 42500 \| 1.1588 \|
	\| 1.1419 \| 0.7167 \| 43000 \| 1.1546 \|
	\| 1.1298 \| 0.725 \| 43500 \| 1.1523 \|
	\| 1.1313 \| 0.7333 \| 44000 \| 1.1489 \|
	\| 1.1322 \| 0.7417 \| 44500 \| 1.1474 \|
	\| 1.1266 \| 0.75 \| 45000 \| 1.1453 \|
	\| 1.1190 \| 0.7583 \| 45500 \| 1.1429 \|
	\| 1.1186 \| 0.7667 \| 46000 \| 1.1399 \|
	\| 1.1122 \| 0.775 \| 46500 \| 1.1377 \|
	\| 1.1145 \| 0.7833 \| 47000 \| 1.1354 \|
	\| 1.1040 \| 0.7917 \| 47500 \| 1.1348 \|
	\| 1.1126 \| 0.8 \| 48000 \| 1.1331 \|
	\| 1.1042 \| 0.8083 \| 48500 \| 1.1304 \|
	\| 1.0946 \| 0.8167 \| 49000 \| 1.1298 \|
	\| 1.0894 \| 0.825 \| 49500 \| 1.1280 \|
	\| 1.0993 \| 0.8333 \| 50000 \| 1.1272 \|
	\| 1.0916 \| 0.8417 \| 50500 \| 1.1261 \|
	\| 1.0844 \| 0.85 \| 51000 \| 1.1234 \|
	\| 1.1222 \| 0.8583 \| 51500 \| 1.1204 \|
	\| 1.1237 \| 0.8667 \| 52000 \| 1.1179 \|
	\| 1.1307 \| 0.875 \| 52500 \| 1.1154 \|
	\| 1.1195 \| 0.8833 \| 53000 \| 1.1141 \|
	\| 1.1164 \| 0.8917 \| 53500 \| 1.1130 \|
	\| 1.1139 \| 0.9 \| 54000 \| 1.1124 \|
	\| 1.1159 \| 0.9083 \| 54500 \| 1.1118 \|
	\| 1.1087 \| 0.9167 \| 55000 \| 1.1112 \|
	\| 1.1082 \| 0.925 \| 55500 \| 1.1104 \|
	\| 1.1036 \| 0.9333 \| 56000 \| 1.1099 \|
	\| 1.1099 \| 0.9417 \| 56500 \| 1.1097 \|
	\| 1.1088 \| 0.95 \| 57000 \| 1.1094 \|
	\| 1.1091 \| 0.9583 \| 57500 \| 1.1091 \|
	\| 1.1052 \| 0.9667 \| 58000 \| 1.1088 \|
	\| 1.1012 \| 0.975 \| 58500 \| 1.1087 \|
	\| 1.1072 \| 0.9833 \| 59000 \| 1.1086 \|
	\| 1.1072 \| 0.9917 \| 59500 \| 1.1086 \|
	\| 1.1053 \| 1.0 \| 60000 \| 1.1086 \|


	### Framework versions

	- Transformers 5.17.0
	- Pytorch 2.11.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.23.2

	---
	library_name: transformers
	tags:
	- generated_from_trainer
	model-index:
	- name: hypa-tiny-keys
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# hypa-tiny-keys

	This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1086

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0003
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 3407
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- training_steps: 60000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 2.8771 \| 0.0083 \| 500 \| 2.8239 \|
	\| 2.3237 \| 0.0167 \| 1000 \| 2.2951 \|
	\| 1.9399 \| 0.025 \| 1500 \| 1.9368 \|
	\| 1.7840 \| 0.0333 \| 2000 \| 1.7695 \|
	\| 1.6826 \| 0.0417 \| 2500 \| 1.6788 \|
	\| 1.6208 \| 0.05 \| 3000 \| 1.6156 \|
	\| 1.5774 \| 0.0583 \| 3500 \| 1.5753 \|
	\| 1.5343 \| 0.0667 \| 4000 \| 1.5389 \|
	\| 1.5108 \| 0.075 \| 4500 \| 1.5089 \|
	\| 1.4918 \| 0.0833 \| 5000 \| 1.4879 \|
	\| 1.4696 \| 0.0917 \| 5500 \| 1.4679 \|
	\| 1.4439 \| 0.1 \| 6000 \| 1.4490 \|
	\| 1.4353 \| 0.1083 \| 6500 \| 1.4347 \|
	\| 1.4301 \| 0.1167 \| 7000 \| 1.4197 \|
	\| 1.4114 \| 0.125 \| 7500 \| 1.4098 \|
	\| 1.3965 \| 0.1333 \| 8000 \| 1.3979 \|
	\| 1.3879 \| 0.1417 \| 8500 \| 1.3883 \|
	\| 1.3754 \| 0.15 \| 9000 \| 1.3759 \|
	\| 1.3746 \| 0.1583 \| 9500 \| 1.3687 \|
	\| 1.3556 \| 0.1667 \| 10000 \| 1.3607 \|
	\| 1.3897 \| 0.175 \| 10500 \| 1.3605 \|
	\| 1.3735 \| 0.1833 \| 11000 \| 1.3588 \|
	\| 1.3561 \| 0.1917 \| 11500 \| 1.3561 \|
	\| 1.3514 \| 0.2 \| 12000 \| 1.3529 \|
	\| 1.3370 \| 0.2083 \| 12500 \| 1.3490 \|
	\| 1.3319 \| 0.2167 \| 13000 \| 1.3470 \|
	\| 1.3339 \| 0.225 \| 13500 \| 1.3440 \|
	\| 1.3161 \| 0.2333 \| 14000 \| 1.3415 \|
	\| 1.3142 \| 0.2417 \| 14500 \| 1.3338 \|
	\| 1.3061 \| 0.25 \| 15000 \| 1.3280 \|
	\| 1.2954 \| 0.2583 \| 15500 \| 1.3279 \|
	\| 1.2961 \| 0.2667 \| 16000 \| 1.3253 \|
	\| 1.2917 \| 0.275 \| 16500 \| 1.3156 \|
	\| 1.2850 \| 0.2833 \| 17000 \| 1.3168 \|
	\| 1.2734 \| 0.2917 \| 17500 \| 1.3135 \|
	\| 1.2637 \| 0.3 \| 18000 \| 1.3080 \|
	\| 1.2708 \| 0.3083 \| 18500 \| 1.3039 \|
	\| 1.2625 \| 0.3167 \| 19000 \| 1.3002 \|
	\| 1.2623 \| 0.325 \| 19500 \| 1.2992 \|
	\| 1.2482 \| 0.3333 \| 20000 \| 1.2961 \|
	\| 1.2453 \| 0.3417 \| 20500 \| 1.2909 \|
	\| 1.2873 \| 0.35 \| 21000 \| 1.2822 \|
	\| 1.2766 \| 0.3583 \| 21500 \| 1.2847 \|
	\| 1.2673 \| 0.3667 \| 22000 \| 1.2796 \|
	\| 1.2618 \| 0.375 \| 22500 \| 1.2755 \|
	\| 1.2543 \| 0.3833 \| 23000 \| 1.2704 \|
	\| 1.2471 \| 0.3917 \| 23500 \| 1.2728 \|
	\| 1.2345 \| 0.4 \| 24000 \| 1.2703 \|
	\| 1.2350 \| 0.4083 \| 24500 \| 1.2690 \|
	\| 1.2337 \| 0.4167 \| 25000 \| 1.2657 \|
	\| 1.2304 \| 0.425 \| 25500 \| 1.2619 \|
	\| 1.2202 \| 0.4333 \| 26000 \| 1.2560 \|
	\| 1.2182 \| 0.4417 \| 26500 \| 1.2521 \|
	\| 1.2137 \| 0.45 \| 27000 \| 1.2488 \|
	\| 1.2118 \| 0.4583 \| 27500 \| 1.2478 \|
	\| 1.2024 \| 0.4667 \| 28000 \| 1.2449 \|
	\| 1.1989 \| 0.475 \| 28500 \| 1.2446 \|
	\| 1.1929 \| 0.4833 \| 29000 \| 1.2421 \|
	\| 1.1942 \| 0.4917 \| 29500 \| 1.2386 \|
	\| 1.1842 \| 0.5 \| 30000 \| 1.2354 \|
	\| 1.1798 \| 0.5083 \| 30500 \| 1.2325 \|
	\| 1.2264 \| 0.5167 \| 31000 \| 1.2270 \|
	\| 1.2130 \| 0.525 \| 31500 \| 1.2215 \|
	\| 1.2058 \| 0.5333 \| 32000 \| 1.2180 \|
	\| 1.1970 \| 0.5417 \| 32500 \| 1.2136 \|
	\| 1.1886 \| 0.55 \| 33000 \| 1.2118 \|
	\| 1.1826 \| 0.5583 \| 33500 \| 1.2106 \|
	\| 1.1798 \| 0.5667 \| 34000 \| 1.2090 \|
	\| 1.1714 \| 0.575 \| 34500 \| 1.2061 \|
	\| 1.1707 \| 0.5833 \| 35000 \| 1.2036 \|
	\| 1.1774 \| 0.5917 \| 35500 \| 1.2013 \|
	\| 1.1602 \| 0.6 \| 36000 \| 1.1985 \|
	\| 1.1625 \| 0.6083 \| 36500 \| 1.1928 \|
	\| 1.1514 \| 0.6167 \| 37000 \| 1.1921 \|
	\| 1.1507 \| 0.625 \| 37500 \| 1.1902 \|
	\| 1.1444 \| 0.6333 \| 38000 \| 1.1878 \|
	\| 1.1425 \| 0.6417 \| 38500 \| 1.1865 \|
	\| 1.1393 \| 0.65 \| 39000 \| 1.1819 \|
	\| 1.1355 \| 0.6583 \| 39500 \| 1.1793 \|
	\| 1.1277 \| 0.6667 \| 40000 \| 1.1765 \|
	\| 1.1314 \| 0.675 \| 40500 \| 1.1780 \|
	\| 1.1272 \| 0.6833 \| 41000 \| 1.1748 \|
	\| 1.1607 \| 0.6917 \| 41500 \| 1.1643 \|
	\| 1.1553 \| 0.7 \| 42000 \| 1.1613 \|
	\| 1.1484 \| 0.7083 \| 42500 \| 1.1588 \|
	\| 1.1419 \| 0.7167 \| 43000 \| 1.1546 \|
	\| 1.1298 \| 0.725 \| 43500 \| 1.1523 \|
	\| 1.1313 \| 0.7333 \| 44000 \| 1.1489 \|
	\| 1.1322 \| 0.7417 \| 44500 \| 1.1474 \|
	\| 1.1266 \| 0.75 \| 45000 \| 1.1453 \|
	\| 1.1190 \| 0.7583 \| 45500 \| 1.1429 \|
	\| 1.1186 \| 0.7667 \| 46000 \| 1.1399 \|
	\| 1.1122 \| 0.775 \| 46500 \| 1.1377 \|
	\| 1.1145 \| 0.7833 \| 47000 \| 1.1354 \|
	\| 1.1040 \| 0.7917 \| 47500 \| 1.1348 \|
	\| 1.1126 \| 0.8 \| 48000 \| 1.1331 \|
	\| 1.1042 \| 0.8083 \| 48500 \| 1.1304 \|
	\| 1.0946 \| 0.8167 \| 49000 \| 1.1298 \|
	\| 1.0894 \| 0.825 \| 49500 \| 1.1280 \|
	\| 1.0993 \| 0.8333 \| 50000 \| 1.1272 \|
	\| 1.0916 \| 0.8417 \| 50500 \| 1.1261 \|
	\| 1.0844 \| 0.85 \| 51000 \| 1.1234 \|
	\| 1.1222 \| 0.8583 \| 51500 \| 1.1204 \|
	\| 1.1237 \| 0.8667 \| 52000 \| 1.1179 \|
	\| 1.1307 \| 0.875 \| 52500 \| 1.1154 \|
	\| 1.1195 \| 0.8833 \| 53000 \| 1.1141 \|
	\| 1.1164 \| 0.8917 \| 53500 \| 1.1130 \|
	\| 1.1139 \| 0.9 \| 54000 \| 1.1124 \|
	\| 1.1159 \| 0.9083 \| 54500 \| 1.1118 \|
	\| 1.1087 \| 0.9167 \| 55000 \| 1.1112 \|
	\| 1.1082 \| 0.925 \| 55500 \| 1.1104 \|
	\| 1.1036 \| 0.9333 \| 56000 \| 1.1099 \|
	\| 1.1099 \| 0.9417 \| 56500 \| 1.1097 \|
	\| 1.1088 \| 0.95 \| 57000 \| 1.1094 \|
	\| 1.1091 \| 0.9583 \| 57500 \| 1.1091 \|
	\| 1.1052 \| 0.9667 \| 58000 \| 1.1088 \|
	\| 1.1012 \| 0.975 \| 58500 \| 1.1087 \|
	\| 1.1072 \| 0.9833 \| 59000 \| 1.1086 \|
	\| 1.1072 \| 0.9917 \| 59500 \| 1.1086 \|
	\| 1.1053 \| 1.0 \| 60000 \| 1.1086 \|


	### Framework versions

	- Transformers 5.17.0
	- Pytorch 2.11.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.23.2