Text Generation
Transformers
Safetensors
English
llama
mindx
mindxtrain
lora
cpu-trained
machine-dream
smollm2
inft
erc-7857
agenticplace
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use PYTHAI/mindXtrain39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PYTHAI/mindXtrain39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PYTHAI/mindXtrain39") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PYTHAI/mindXtrain39") model = AutoModelForCausalLM.from_pretrained("PYTHAI/mindXtrain39", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PYTHAI/mindXtrain39 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PYTHAI/mindXtrain39" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PYTHAI/mindXtrain39", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PYTHAI/mindXtrain39
- SGLang
How to use PYTHAI/mindXtrain39 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 "PYTHAI/mindXtrain39" \ --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": "PYTHAI/mindXtrain39", "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 "PYTHAI/mindXtrain39" \ --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": "PYTHAI/mindXtrain39", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PYTHAI/mindXtrain39 with Docker Model Runner:
docker model run hf.co/PYTHAI/mindXtrain39
| cpu_throttle overridden: percent=33 nice=19 | |
| Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. | |
| [transformers] `torch_dtype` is deprecated! Use `dtype` instead! | |
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| [transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. | |
| [RANK 0] Padding-free training is enabled, but the attention implementation is not set to a supported flash attention variant. Padding-free training flattens batches into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to unexpected behavior. To ensure compatibility, set `attn_implementation` in the model configuration to one of these supported options or verify that your attention mechanism can handle flattened sequences. | |
| [RANK 0] You are using packing, but the attention implementation is not set to a supported flash attention variant. Packing gathers multiple samples into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to cross-contamination between samples. To avoid this, either disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration to one of these supported options. | |
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| Packing eval dataset: 100%|██████████| 52/52 [00:00<00:00, 17791.32 examples/s] | |
| [transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 0}. | |
| 0%| | 0/116 [00:00<?, ?it/s]/home/mindx/mindXtrain/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used. | |
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| 24%|██▍ | 28/116 [14:55<46:01, 31.38s/it]{'loss': '2.325', 'grad_norm': '0.4654', 'learning_rate': '0', 'entropy': '2.052', 'num_tokens': '3009', 'mean_token_accuracy': '0.5974', 'epoch': '0.01739'} | |
| {'loss': '2.319', 'grad_norm': '0.45', 'learning_rate': '2.5e-05', 'entropy': '2.054', 'num_tokens': '6166', 'mean_token_accuracy': '0.6016', 'epoch': '0.03478'} | |
| {'loss': '2.3', 'grad_norm': '0.4626', 'learning_rate': '5e-05', 'entropy': '2.037', 'num_tokens': '9241', 'mean_token_accuracy': '0.6015', 'epoch': '0.05217'} | |
| {'loss': '2.304', 'grad_norm': '0.4594', 'learning_rate': '7.5e-05', 'entropy': '2.043', 'num_tokens': '1.236e+04', 'mean_token_accuracy': '0.6028', 'epoch': '0.06957'} | |
| {'loss': '2.304', 'grad_norm': '0.4834', 'learning_rate': '0.0001', 'entropy': '2.052', 'num_tokens': '1.54e+04', 'mean_token_accuracy': '0.5986', 'epoch': '0.08696'} | |
| {'loss': '2.281', 'grad_norm': '0.4952', 'learning_rate': '9.911e-05', 'entropy': '2.03', 'num_tokens': '1.834e+04', 'mean_token_accuracy': '0.5978', 'epoch': '0.1043'} | |
| {'loss': '2.258', 'grad_norm': '0.4734', 'learning_rate': '9.821e-05', 'entropy': '2.02', 'num_tokens': '2.139e+04', 'mean_token_accuracy': '0.6039', 'epoch': '0.1217'} | |
| {'loss': '2.257', 'grad_norm': '0.4711', 'learning_rate': '9.732e-05', 'entropy': '2.023', 'num_tokens': '2.437e+04', 'mean_token_accuracy': '0.6016', 'epoch': '0.1391'} | |
| {'loss': '2.255', 'grad_norm': '0.447', 'learning_rate': '9.643e-05', 'entropy': '2.04', 'num_tokens': '2.74e+04', 'mean_token_accuracy': '0.6059', 'epoch': '0.1565'} | |
| {'loss': '2.207', 'grad_norm': '0.4243', 'learning_rate': '9.554e-05', 'entropy': '1.995', 'num_tokens': '3.044e+04', 'mean_token_accuracy': '0.6123', 'epoch': '0.1739'} | |
| {'loss': '2.197', 'grad_norm': '0.409', 'learning_rate': '9.464e-05', 'entropy': '1.995', 'num_tokens': '3.343e+04', 'mean_token_accuracy': '0.6075', 'epoch': '0.1913'} | |
| {'loss': '2.198', 'grad_norm': '0.385', 'learning_rate': '9.375e-05', 'entropy': '1.993', 'num_tokens': '3.648e+04', 'mean_token_accuracy': '0.6063', 'epoch': '0.2087'} | |
| {'loss': '2.184', 'grad_norm': '0.385', 'learning_rate': '9.286e-05', 'entropy': '1.991', 'num_tokens': '3.946e+04', 'mean_token_accuracy': '0.6067', 'epoch': '0.2261'} | |
| {'loss': '2.156', 'grad_norm': '0.3798', 'learning_rate': '9.196e-05', 'entropy': '1.981', 'num_tokens': '4.25e+04', 'mean_token_accuracy': '0.6215', 'epoch': '0.2435'} | |
| {'loss': '2.14', 'grad_norm': '0.3655', 'learning_rate': '9.107e-05', 'entropy': '1.951', 'num_tokens': '4.555e+04', 'mean_token_accuracy': '0.6124', 'epoch': '0.2609'} | |
| {'loss': '2.129', 'grad_norm': '0.3608', 'learning_rate': '9.018e-05', 'entropy': '1.941', 'num_tokens': '4.865e+04', 'mean_token_accuracy': '0.6234', 'epoch': '0.2783'} | |
| {'loss': '2.121', 'grad_norm': '0.3709', 'learning_rate': '8.929e-05', 'entropy': '1.957', 'num_tokens': '5.17e+04', 'mean_token_accuracy': '0.6175', 'epoch': '0.2957'} | |
| {'loss': '2.115', 'grad_norm': '0.379', 'learning_rate': '8.839e-05', 'entropy': '1.944', 'num_tokens': '5.472e+04', 'mean_token_accuracy': '0.6188', 'epoch': '0.313'} | |
| {'loss': '2.096', 'grad_norm': '0.3982', 'learning_rate': '8.75e-05', 'entropy': '1.963', 'num_tokens': '5.768e+04', 'mean_token_accuracy': '0.6125', 'epoch': '0.3304'} | |
| {'loss': '2.087', 'grad_norm': '0.4', 'learning_rate': '8.661e-05', 'entropy': '1.938', 'num_tokens': '6.066e+04', 'mean_token_accuracy': '0.6188', 'epoch': '0.3478'} | |
| {'loss': '2.076', 'grad_norm': '0.3924', 'learning_rate': '8.571e-05', 'entropy': '1.934', 'num_tokens': '6.369e+04', 'mean_token_accuracy': '0.6201', 'epoch': '0.3652'} | |
| {'loss': '2.069', 'grad_norm': '0.4229', 'learning_rate': '8.482e-05', 'entropy': '1.947', 'num_tokens': '6.667e+04', 'mean_token_accuracy': '0.6177', 'epoch': '0.3826'} | |
| {'loss': '2.019', 'grad_norm': '0.4232', 'learning_rate': '8.393e-05', 'entropy': '1.881', 'num_tokens': '6.976e+04', 'mean_token_accuracy': '0.6257', 'epoch': '0.4'} | |
| {'loss': '2.003', 'grad_norm': '0.4416', 'learning_rate': '8.304e-05', 'entropy': '1.888', 'num_tokens': '7.277e+04', 'mean_token_accuracy': '0.6249', 'epoch': '0.4174'} | |
| {'loss': '2.003', 'grad_norm': '0.4683', 'learning_rate': '8.214e-05', 'entropy': '1.888', 'num_tokens': '7.576e+04', 'mean_token_accuracy': '0.6242', 'epoch': '0.4348'} | |
| {'loss': '1.981', 'grad_norm': '0.4952', 'learning_rate': '8.125e-05', 'entropy': '1.881', 'num_tokens': '7.881e+04', 'mean_token_accuracy': '0.6311', 'epoch': '0.4522'} | |
| {'loss': '1.97', 'grad_norm': '0.5053', 'learning_rate': '8.036e-05', 'entropy': '1.871', 'num_tokens': '8.187e+04', 'mean_token_accuracy': '0.6296', 'epoch': '0.4696'} | |
| {'loss': '1.943', 'grad_norm': '0.4698', 'learning_rate': '7.946e-05', 'entropy': '1.858', 'num_tokens': '8.486e+04', 'mean_token_accuracy': '0.629', 'epoch': '0.487'} | |
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| 48%|████▊ | 56/116 [31:32<32:12, 32.21s/it]{'eval_loss': '1.929', 'eval_runtime': '97.08', 'eval_samples_per_second': '0.536', 'eval_steps_per_second': '0.536', 'eval_entropy': '1.861', 'eval_num_tokens': '8.486e+04', 'eval_mean_token_accuracy': '0.6287', 'epoch': '0.487'} | |
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| {'loss': '1.894', 'grad_norm': '0.388', 'learning_rate': '7.679e-05', 'entropy': '1.842', 'num_tokens': '9.388e+04', 'mean_token_accuracy': '0.6311', 'epoch': '0.5391'} | |
| {'loss': '1.904', 'grad_norm': '0.367', 'learning_rate': '7.589e-05', 'entropy': '1.842', 'num_tokens': '9.699e+04', 'mean_token_accuracy': '0.6352', 'epoch': '0.5565'} | |
| {'loss': '1.884', 'grad_norm': '0.3763', 'learning_rate': '7.5e-05', 'entropy': '1.846', 'num_tokens': '1e+05', 'mean_token_accuracy': '0.6326', 'epoch': '0.5739'} | |
| {'loss': '1.857', 'grad_norm': '0.3783', 'learning_rate': '7.411e-05', 'entropy': '1.831', 'num_tokens': '1.03e+05', 'mean_token_accuracy': '0.6351', 'epoch': '0.5913'} | |
| {'loss': '1.847', 'grad_norm': '0.3721', 'learning_rate': '7.321e-05', 'entropy': '1.816', 'num_tokens': '1.061e+05', 'mean_token_accuracy': '0.6389', 'epoch': '0.6087'} | |
| {'loss': '1.836', 'grad_norm': '0.3744', 'learning_rate': '7.232e-05', 'entropy': '1.808', 'num_tokens': '1.091e+05', 'mean_token_accuracy': '0.6372', 'epoch': '0.6261'} | |
| {'loss': '1.823', 'grad_norm': '0.3716', 'learning_rate': '7.143e-05', 'entropy': '1.796', 'num_tokens': '1.121e+05', 'mean_token_accuracy': '0.639', 'epoch': '0.6435'} | |
| {'loss': '1.808', 'grad_norm': '0.3667', 'learning_rate': '7.054e-05', 'entropy': '1.789', 'num_tokens': '1.152e+05', 'mean_token_accuracy': '0.6436', 'epoch': '0.6609'} | |
| {'loss': '1.81', 'grad_norm': '0.3616', 'learning_rate': '6.964e-05', 'entropy': '1.797', 'num_tokens': '1.182e+05', 'mean_token_accuracy': '0.6383', 'epoch': '0.6783'} | |
| {'loss': '1.785', 'grad_norm': '0.3761', 'learning_rate': '6.875e-05', 'entropy': '1.8', 'num_tokens': '1.212e+05', 'mean_token_accuracy': '0.6383', 'epoch': '0.6957'} | |
| {'loss': '1.756', 'grad_norm': '0.3711', 'learning_rate': '6.786e-05', 'entropy': '1.778', 'num_tokens': '1.242e+05', 'mean_token_accuracy': '0.6404', 'epoch': '0.713'} | |
| {'loss': '1.755', 'grad_norm': '0.3601', 'learning_rate': '6.696e-05', 'entropy': '1.774', 'num_tokens': '1.272e+05', 'mean_token_accuracy': '0.6392', 'epoch': '0.7304'} | |
| {'loss': '1.755', 'grad_norm': '0.3625', 'learning_rate': '6.607e-05', 'entropy': '1.776', 'num_tokens': '1.302e+05', 'mean_token_accuracy': '0.6346', 'epoch': '0.7478'} | |
| {'loss': '1.732', 'grad_norm': '0.3582', 'learning_rate': '6.518e-05', 'entropy': '1.753', 'num_tokens': '1.332e+05', 'mean_token_accuracy': '0.644', 'epoch': '0.7652'} | |
| {'loss': '1.721', 'grad_norm': '0.3615', 'learning_rate': '6.429e-05', 'entropy': '1.76', 'num_tokens': '1.362e+05', 'mean_token_accuracy': '0.6448', 'epoch': '0.7826'} | |
| {'loss': '1.716', 'grad_norm': '0.3503', 'learning_rate': '6.339e-05', 'entropy': '1.745', 'num_tokens': '1.392e+05', 'mean_token_accuracy': '0.6358', 'epoch': '0.8'} | |
| {'loss': '1.702', 'grad_norm': '0.3443', 'learning_rate': '6.25e-05', 'entropy': '1.727', 'num_tokens': '1.423e+05', 'mean_token_accuracy': '0.6476', 'epoch': '0.8174'} | |
| {'loss': '1.695', 'grad_norm': '0.3419', 'learning_rate': '6.161e-05', 'entropy': '1.73', 'num_tokens': '1.453e+05', 'mean_token_accuracy': '0.6471', 'epoch': '0.8348'} | |
| {'loss': '1.693', 'grad_norm': '0.3415', 'learning_rate': '6.071e-05', 'entropy': '1.739', 'num_tokens': '1.484e+05', 'mean_token_accuracy': '0.6439', 'epoch': '0.8522'} | |
| {'loss': '1.685', 'grad_norm': '0.3367', 'learning_rate': '5.982e-05', 'entropy': '1.729', 'num_tokens': '1.515e+05', 'mean_token_accuracy': '0.6447', 'epoch': '0.8696'} | |
| {'loss': '1.657', 'grad_norm': '0.3387', 'learning_rate': '5.893e-05', 'entropy': '1.72', 'num_tokens': '1.545e+05', 'mean_token_accuracy': '0.6501', 'epoch': '0.887'} | |
| {'loss': '1.641', 'grad_norm': '0.344', 'learning_rate': '5.804e-05', 'entropy': '1.713', 'num_tokens': '1.576e+05', 'mean_token_accuracy': '0.6503', 'epoch': '0.9043'} | |
| {'loss': '1.617', 'grad_norm': '0.3398', 'learning_rate': '5.714e-05', 'entropy': '1.705', 'num_tokens': '1.606e+05', 'mean_token_accuracy': '0.6555', 'epoch': '0.9217'} | |
| {'loss': '1.619', 'grad_norm': '0.3491', 'learning_rate': '5.625e-05', 'entropy': '1.706', 'num_tokens': '1.636e+05', 'mean_token_accuracy': '0.6601', 'epoch': '0.9391'} | |
| {'loss': '1.608', 'grad_norm': '0.3531', 'learning_rate': '5.536e-05', 'entropy': '1.71', 'num_tokens': '1.666e+05', 'mean_token_accuracy': '0.6652', 'epoch': '0.9565'} | |
| {'loss': '1.593', 'grad_norm': '0.3431', 'learning_rate': '5.446e-05', 'entropy': '1.697', 'num_tokens': '1.696e+05', 'mean_token_accuracy': '0.6651', 'epoch': '0.9739'} | |
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| super().__init__(loader) | |
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| 72%|███████▏ | 84/116 [48:13<17:16, 32.40s/it]{'eval_loss': '1.578', 'eval_runtime': '101.6', 'eval_samples_per_second': '0.512', 'eval_steps_per_second': '0.512', 'eval_entropy': '1.679', 'eval_num_tokens': '1.696e+05', 'eval_mean_token_accuracy': '0.6597', 'epoch': '0.9739'} | |
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| {'loss': '1.383', 'grad_norm': '0.35', 'learning_rate': '3.036e-05', 'entropy': '1.551', 'num_tokens': '2.5e+05', 'mean_token_accuracy': '0.6982', 'epoch': '1.435'} | |
| {'loss': '1.371', 'grad_norm': '0.3513', 'learning_rate': '2.946e-05', 'entropy': '1.551', 'num_tokens': '2.531e+05', 'mean_token_accuracy': '0.6993', 'epoch': '1.452'} | |
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| 97%|█████████▋| 112/116 [1:04:52<02:08, 32.14s/it]{'eval_loss': '1.337', 'eval_runtime': '99.21', 'eval_samples_per_second': '0.524', 'eval_steps_per_second': '0.524', 'eval_entropy': '1.529', 'eval_num_tokens': '2.531e+05', 'eval_mean_token_accuracy': '0.7008', 'epoch': '1.452'} | |
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| {'loss': '1.268', 'grad_norm': '0.3723', 'learning_rate': '8.929e-06', 'entropy': '1.481', 'num_tokens': '3.227e+05', 'mean_token_accuracy': '0.7034', 'epoch': '1.852'} | |
| {'loss': '1.227', 'grad_norm': '0.3826', 'learning_rate': '8.036e-06', 'entropy': '1.447', 'num_tokens': '3.257e+05', 'mean_token_accuracy': '0.7107', 'epoch': '1.87'} | |
| {'loss': '1.24', 'grad_norm': '0.3853', 'learning_rate': '7.143e-06', 'entropy': '1.454', 'num_tokens': '3.288e+05', 'mean_token_accuracy': '0.7155', 'epoch': '1.887'} | |
| {'loss': '1.238', 'grad_norm': '0.3807', 'learning_rate': '6.25e-06', 'entropy': '1.453', 'num_tokens': '3.318e+05', 'mean_token_accuracy': '0.7101', 'epoch': '1.904'} | |
| {'loss': '1.23', 'grad_norm': '0.3776', 'learning_rate': '5.357e-06', 'entropy': '1.439', 'num_tokens': '3.348e+05', 'mean_token_accuracy': '0.7141', 'epoch': '1.922'} | |
| {'loss': '1.257', 'grad_norm': '0.378', 'learning_rate': '4.464e-06', 'entropy': '1.459', 'num_tokens': '3.38e+05', 'mean_token_accuracy': '0.717', 'epoch': '1.939'} | |
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| [A | |
| 97%|█████████▋| 112/116 [1:06:31<02:08, 32.14s/it] | |
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| 100%|██████████| 116/116 [1:08:22<00:00, 37.52s/it]{'eval_loss': '1.227', 'eval_runtime': '99.61', 'eval_samples_per_second': '0.522', 'eval_steps_per_second': '0.522', 'eval_entropy': '1.447', 'eval_num_tokens': '3.38e+05', 'eval_mean_token_accuracy': '0.7102', 'epoch': '1.939'} | |
| {'loss': '1.222', 'grad_norm': '0.3988', 'learning_rate': '3.571e-06', 'entropy': '1.453', 'num_tokens': '3.409e+05', 'mean_token_accuracy': '0.7062', 'epoch': '1.957'} | |
| {'loss': '1.236', 'grad_norm': '0.3919', 'learning_rate': '2.679e-06', 'entropy': '1.469', 'num_tokens': '3.438e+05', 'mean_token_accuracy': '0.705', 'epoch': '1.974'} | |
| {'loss': '1.262', 'grad_norm': '0.3766', 'learning_rate': '1.786e-06', 'entropy': '1.462', 'num_tokens': '3.469e+05', 'mean_token_accuracy': '0.7158', 'epoch': '1.991'} | |
| {'loss': '1.255', 'grad_norm': '0.3805', 'learning_rate': '8.929e-07', 'entropy': '1.467', 'num_tokens': '3.485e+05', 'mean_token_accuracy': '0.7127', 'epoch': '2'} | |
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| [A | |
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| {'eval_loss': '1.225', 'eval_runtime': '97.16', 'eval_samples_per_second': '0.535', 'eval_steps_per_second': '0.535', 'eval_entropy': '1.445', 'eval_num_tokens': '3.485e+05', 'eval_mean_token_accuracy': '0.7107', 'epoch': '2'} | |
| {'train_runtime': '4201', 'train_samples_per_second': '0.219', 'train_steps_per_second': '0.028', 'train_loss': '1.65', 'epoch': '2'} | |
| checkpoint: out/runs/mindx_fallback_qwen3_1_5b_cpu_real/checkpoint | |