Instructions to use Gege24/environment_test_affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Gege24/environment_test_affine with PEFT:
```
Base model is not found.
```

How to use Gege24/environment_test_affine with Transformers:

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

pipe = pipeline("text-generation", model="Gege24/environment_test_affine")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gege24/environment_test_affine")
model = AutoModelForCausalLM.from_pretrained("Gege24/environment_test_affine")
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

vLLM

How to use Gege24/environment_test_affine with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gege24/environment_test_affine"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gege24/environment_test_affine",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gege24/environment_test_affine

SGLang

How to use Gege24/environment_test_affine 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 "Gege24/environment_test_affine" \
    --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": "Gege24/environment_test_affine",
		"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 "Gege24/environment_test_affine" \
        --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": "Gege24/environment_test_affine",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Gege24/environment_test_affine with Docker Model Runner:
```
docker model run hf.co/Gege24/environment_test_affine
```

environment_test_affine / debug.log

Gege24

Upload task output 1

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	[2026-01-27 12:10:11,774] [DEBUG] [axolotl.utils.config.resolve_dtype:66] [PID:168] bf16 support detected, enabling for this configuration.
	[2026-01-27 12:10:11,777] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:168] baseline 0.000GB ()
	[2026-01-27 12:10:11,777] [INFO] [axolotl.cli.config.load_cfg:259] [PID:168] config:
	{
	"activation_offloading": false,
	"adapter": "lora",
	"axolotl_config_path": "/workspace/axolotl/configs/1.yml",
	"base_model": "/cache/models/Qwen--Qwen2.5-3B-Instruct",
	"base_model_config": "/cache/models/Qwen--Qwen2.5-3B-Instruct",
	"batch_size": 8,
	"bf16": true,
	"capabilities": {
	"bf16": true,
	"compute_capability": "sm_90",
	"fp8": true,
	"n_gpu": 1,
	"n_node": 1
	},
	"chat_template": "llama3",
	"context_parallel_size": 1,
	"dataloader_num_workers": 1,
	"dataloader_pin_memory": true,
	"dataloader_prefetch_factor": 256,
	"dataset_num_proc": 32,
	"datasets": [
	{
	"data_files": [
	"1_train_data.json"
	],
	"ds_type": "json",
	"message_property_mappings": {
	"content": "content",
	"role": "role"
	},
	"path": "/workspace/axolotl/data",
	"split": "train",
	"trust_remote_code": false
	}
	],
	"ddp": false,
	"device": "cuda:0",
	"dion_rank_fraction": 1.0,
	"dion_rank_multiple_of": 1,
	"env_capabilities": {
	"torch_version": "2.8.0"
	},
	"eval_batch_size": 8,
	"eval_causal_lm_metrics": [
	"sacrebleu",
	"comet",
	"ter",
	"chrf"
	],
	"eval_max_new_tokens": 128,
	"eval_strategy": "no",
	"eval_table_size": 0,
	"experimental_skip_move_to_device": true,
	"flash_attention": false,
	"fp16": false,
	"gradient_accumulation_steps": 1,
	"gradient_checkpointing": false,
	"group_by_length": false,
	"include_tkps": true,
	"is_falcon_derived_model": false,
	"is_llama_derived_model": false,
	"is_mistral_derived_model": false,
	"learning_rate": 7e-06,
	"lisa_layers_attribute": "model.layers",
	"load_best_model_at_end": false,
	"load_in_4bit": false,
	"load_in_8bit": false,
	"local_rank": 0,
	"logging_steps": 1,
	"lora_alpha": 32,
	"lora_dropout": 0.0,
	"lora_r": 64,
	"lora_target_linear": true,
	"loraplus_lr_embedding": 1e-06,
	"lr_scheduler": "cosine",
	"max_grad_norm": 1.0,
	"max_steps": 100000,
	"mean_resizing_embeddings": false,
	"micro_batch_size": 8,
	"mlflow_experiment_name": "/workspace/axolotl/data/1_train_data.json",
	"model_config_type": "qwen2",
	"num_epochs": 1.0,
	"optimizer": "adamw_bnb_8bit",
	"otel_metrics_host": "localhost",
	"otel_metrics_port": 8000,
	"output_dir": "/app/checkpoints/1/environment_test_affine",
	"pad_to_sequence_len": true,
	"pretrain_multipack_attn": true,
	"profiler_steps_start": 0,
	"qlora_sharded_model_loading": false,
	"ray_num_workers": 1,
	"resources_per_worker": {
	"GPU": 1
	},
	"rl": "grpo",
	"sample_packing": false,
	"sample_packing_bin_size": 200,
	"sample_packing_group_size": 100000,
	"save_only_model": false,
	"save_safetensors": true,
	"save_steps": 10,
	"save_total_limit": 1,
	"sequence_len": 24000,
	"shuffle_before_merging_datasets": false,
	"shuffle_merged_datasets": true,
	"skip_prepare_dataset": false,
	"special_tokens": {
	"bos_token": "<\|im_end\|>"
	},
	"streaming_multipack_buffer_size": 10000,
	"strict": false,
	"tensor_parallel_size": 1,
	"tf32": false,
	"tiled_mlp_use_original_mlp": true,
	"tokenizer_config": "/cache/models/Qwen--Qwen2.5-3B-Instruct",
	"tokenizer_save_jinja_files": true,
	"tokenizer_type": "AutoTokenizer",
	"torch_dtype": "torch.bfloat16",
	"train_on_inputs": false,
	"trl": {
	"beta": 0.001,
	"log_completions": false,
	"mask_truncated_completions": false,
	"max_completion_length": 512,
	"num_generations": 8,
	"ref_model_mixup_alpha": 0.9,
	"ref_model_sync_steps": 64,
	"reward_funcs": [
	"affine_game.rollout_reward_func"
	],
	"reward_weights": [
	1.0
	],
	"rollout_func": "affine_game.rollout_first_prompt_and_completion",
	"scale_rewards": true,
	"sync_ref_model": false,
	"temperature": 0.7,
	"use_vllm": true,
	"vllm_enable_sleep_mode": false,
	"vllm_mode": "colocate",
	"vllm_server_host": "0.0.0.0",
	"vllm_server_port": 8000
	},
	"trust_remote_code": true,
	"type_of_model": "AutoModelForCausalLM",
	"use_mlflow": true,
	"use_otel_metrics": false,
	"use_ray": false,
	"use_wandb": true,
	"val_set_size": 0.0,
	"vllm": {
	"device": "auto",
	"dtype": "auto",
	"enable_prefix_caching": false,
	"gpu_memory_utilization": 0.15,
	"host": "0.0.0.0",
	"max_model_len": 24000,
	"port": 8000,
	"tensor_parallel_size": 1
	},
	"wandb_mode": "online",
	"wandb_name": "1_environment_test_affine",
	"wandb_project": "Affine-GAME-Tests",
	"warmup_steps": 20,
	"weight_decay": 0.0,
	"world_size": 1
	}
	[2026-01-27 12:10:12,210] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:285] [PID:168] EOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:12,210] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:286] [PID:168] BOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:12,210] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:287] [PID:168] PAD: 151643 / <\|endoftext\|>
	[2026-01-27 12:10:12,210] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:288] [PID:168] UNK: None / None
	[2026-01-27 12:10:12,210] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:481] [PID:168] Unable to find prepared dataset in last_run_prepared/ba0ae834220c702ae7aefbdbfde66c85
	Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 1000 examples [00:00, 123307.48 examples/s]
	[2026-01-27 12:10:12,835] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:285] [PID:168] EOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:12,835] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:286] [PID:168] BOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:12,835] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:287] [PID:168] PAD: 151643 / <\|endoftext\|>
	[2026-01-27 12:10:12,835] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:288] [PID:168] UNK: None / None
	Dropping Long Sequences (num_proc=32): 0%\| \| 0/1000 [00:00<?, ? examples/s] Dropping Long Sequences (num_proc=32): 3%\|▎ \| 32/1000 [00:00<00:27, 35.34 examples/s] Dropping Long Sequences (num_proc=32): 10%\|▉ \| 96/1000 [00:01<00:07, 117.01 examples/s] Dropping Long Sequences (num_proc=32): 16%\|█▌ \| 160/1000 [00:01<00:04, 186.99 examples/s] Dropping Long Sequences (num_proc=32): 22%\|██▏ \| 224/1000 [00:01<00:03, 227.92 examples/s] Dropping Long Sequences (num_proc=32): 29%\|██▊ \| 287/1000 [00:01<00:02, 277.18 examples/s] Dropping Long Sequences (num_proc=32): 35%\|███▍ \| 349/1000 [00:01<00:02, 312.62 examples/s] Dropping Long Sequences (num_proc=32): 41%\|████ \| 411/1000 [00:01<00:01, 333.29 examples/s] Dropping Long Sequences (num_proc=32): 47%\|████▋ \| 473/1000 [00:01<00:01, 338.99 examples/s] Dropping Long Sequences (num_proc=32): 54%\|█████▎ \| 535/1000 [00:02<00:01, 347.40 examples/s] Dropping Long Sequences (num_proc=32): 60%\|█████▉ \| 597/1000 [00:02<00:01, 350.00 examples/s] Dropping Long Sequences (num_proc=32): 66%\|██████▌ \| 659/1000 [00:02<00:00, 362.42 examples/s] Dropping Long Sequences (num_proc=32): 72%\|███████▏ \| 721/1000 [00:02<00:00, 378.68 examples/s] Dropping Long Sequences (num_proc=32): 78%\|███████▊ \| 783/1000 [00:02<00:00, 359.18 examples/s] Dropping Long Sequences (num_proc=32): 84%\|████████▍ \| 845/1000 [00:02<00:00, 384.00 examples/s] Dropping Long Sequences (num_proc=32): 91%\|█████████ \| 907/1000 [00:03<00:00, 429.74 examples/s] Dropping Long Sequences (num_proc=32): 97%\|█████████▋\| 969/1000 [00:03<00:00, 433.14 examples/s] Dropping Long Sequences (num_proc=32): 100%\|██████████\| 1000/1000 [00:03<00:00, 278.63 examples/s]
	Saving the dataset (0/3 shards): 0%\| \| 0/1000 [00:00<?, ? examples/s] Saving the dataset (1/3 shards): 33%\|███▎ \| 334/1000 [00:00<00:00, 3720.68 examples/s] Saving the dataset (2/3 shards): 67%\|██████▋ \| 667/1000 [00:00<00:00, 7231.20 examples/s] Saving the dataset (3/3 shards): 100%\|██████████\| 1000/1000 [00:00<00:00, 10699.73 examples/s] Saving the dataset (3/3 shards): 100%\|██████████\| 1000/1000 [00:00<00:00, 5825.09 examples/s]
	[2026-01-27 12:10:16,798] [DEBUG] [axolotl.train.setup_model_and_tokenizer:70] [PID:168] loading tokenizer... /cache/models/Qwen--Qwen2.5-3B-Instruct
	[2026-01-27 12:10:16,974] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:285] [PID:168] EOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:16,974] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:286] [PID:168] BOS: 151645 / <\|im_end\|>
	[2026-01-27 12:10:16,974] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:287] [PID:168] PAD: 151643 / <\|endoftext\|>
	[2026-01-27 12:10:16,974] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:288] [PID:168] UNK: None / None
	[2026-01-27 12:10:16,974] [DEBUG] [axolotl.train.setup_model_and_tokenizer:82] [PID:168] Loading model
	[2026-01-27 12:10:16,986] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:87] [PID:168] Patched Trainer.evaluation_loop with nanmean loss calculation
	[2026-01-27 12:10:16,987] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:138] [PID:168] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
	Loading checkpoint shards: 0%\| \| 0/2 [00:00<?, ?it/s] Loading checkpoint shards: 100%\|██████████\| 2/2 [00:00<00:00, 86.03it/s]
	[2026-01-27 12:10:17,609] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:347] [PID:168] Converting modules to torch.bfloat16
	[2026-01-27 12:10:18,005] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:168] Memory usage after model load 0.000GB ()
	[2026-01-27 12:10:18,005] [INFO] [axolotl.loaders.adapter.load_lora:81] [PID:168] found linear modules: ['down_proj', 'gate_proj', 'k_proj', 'o_proj', 'q_proj', 'up_proj', 'v_proj']
	trainable params: 119,734,272 \|\| all params: 3,205,672,960 \|\| trainable%: 3.7351
	[2026-01-27 12:10:18,865] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:168] after adapters 0.000GB ()
	[2026-01-27 12:10:19,505] [DEBUG] [axolotl.train.setup_reference_model:126] [PID:168] Passing model_ref: None to RL trainer
	[2026-01-27 12:10:25,633] [WARNING] [py.warnings._showwarnmsg:110] [PID:168] /workspace/axolotl/src/axolotl/core/trainers/mixins/optimizer.py:209: UserWarning: You are importing from 'rollout_func', which is an experimental feature. This API may change or be removed at any time without prior notice. Silence this warning by setting environment variable TRL_EXPERIMENTAL_SILENCE=1.
	super().__init__(args, *kwargs)

	Loading safetensors checkpoint shards: 0% Completed \| 0/2 [00:00<?, ?it/s]
	Loading safetensors checkpoint shards: 50% Completed \| 1/2 [00:00<00:00, 4.17it/s]
	Loading safetensors checkpoint shards: 100% Completed \| 2/2 [00:00<00:00, 2.61it/s]
	Loading safetensors checkpoint shards: 100% Completed \| 2/2 [00:00<00:00, 2.77it/s]

	Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 0%\| \| 0/5 [00:00<?, ?it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 80%\|████████ \| 4/5 [00:00<00:00, 34.65it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%\|██████████\| 5/5 [00:00<00:00, 32.94it/s]
	[2026-01-27 12:11:01,350] [INFO] [axolotl.train.save_initial_configs:413] [PID:168] Pre-saving adapter config to /app/checkpoints/1/environment_test_affine...
	[2026-01-27 12:11:01,350] [INFO] [axolotl.train.save_initial_configs:417] [PID:168] Pre-saving tokenizer to /app/checkpoints/1/environment_test_affine...
	[2026-01-27 12:11:01,463] [INFO] [axolotl.train.save_initial_configs:422] [PID:168] Pre-saving model config to /app/checkpoints/1/environment_test_affine...
	[2026-01-27 12:11:01,466] [INFO] [axolotl.train.execute_training:212] [PID:168] Starting trainer...
	wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from WANDB_API_KEY.
	wandb: Currently logged in as: bkbvol (bkbvol-bittensor) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
	wandb: setting up run 34sye9hd
	wandb: Tracking run with wandb version 0.24.0
	wandb: Run data is saved locally in /workspace/axolotl/wandb/run-20260127_121102-34sye9hd
	wandb: Run `wandb offline` to turn off syncing.
	wandb: Syncing run 1_environment_test_affine
	wandb: ⭐️ View project at https://wandb.ai/bkbvol-bittensor/Affine-GAME-Tests
	wandb: 🚀 View run at https://wandb.ai/bkbvol-bittensor/Affine-GAME-Tests/runs/34sye9hd
	wandb: Detected [huggingface_hub.inference, openai] in use.
	wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
	wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
	Traceback (most recent call last):
	File "<frozen runpy>", line 198, in _run_module_as_main
	File "<frozen runpy>", line 88, in _run_code
	File "/workspace/axolotl/src/axolotl/cli/train.py", line 121, in <module>
	fire.Fire(do_cli)
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 135, in Fire
	component_trace = _Fire(component, args, parsed_flag_args, context, name)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 468, in _Fire
	component, remaining_args = _CallAndUpdateTrace(
	^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 684, in _CallAndUpdateTrace
	component = fn(varargs, *kwargs)
	^^^^^^^^^^^^^^^^^^^^^^
	File "/workspace/axolotl/src/axolotl/cli/train.py", line 88, in do_cli
	return do_train(parsed_cfg, parsed_cli_args)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/workspace/axolotl/src/axolotl/cli/train.py", line 45, in do_train
	model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/workspace/axolotl/src/axolotl/telemetry/errors.py", line 124, in wrapper
	return func(args, *kwargs)
	^^^^^^^^^^^^^^^^^^^^^
	File "/workspace/axolotl/src/axolotl/train.py", line 598, in train
	execute_training(cfg, trainer, resume_from_checkpoint)
	File "/workspace/axolotl/src/axolotl/train.py", line 213, in execute_training
	trainer.train(resume_from_checkpoint=resume_from_checkpoint)
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer.py", line 2325, in train
	return inner_training_loop(
	^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer.py", line 2573, in _inner_training_loop
	self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer_callback.py", line 506, in on_train_begin
	return self.call_event("on_train_begin", args, state, control)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer_callback.py", line 556, in call_event
	result = getattr(callback, event)(
	^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/integrations/integration_utils.py", line 1489, in on_train_begin
	self.setup(args, state, model)
	File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/integrations/integration_utils.py", line 1430, in setup
	if not self._ml_flow.is_tracking_uri_set():
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	AttributeError: module 'mlflow' has no attribute 'is_tracking_uri_set'
	[rank0]: Traceback (most recent call last):
	[rank0]: File "<frozen runpy>", line 198, in _run_module_as_main
	[rank0]: File "<frozen runpy>", line 88, in _run_code
	[rank0]: File "/workspace/axolotl/src/axolotl/cli/train.py", line 121, in <module>
	[rank0]: fire.Fire(do_cli)
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 135, in Fire
	[rank0]: component_trace = _Fire(component, args, parsed_flag_args, context, name)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 468, in _Fire
	[rank0]: component, remaining_args = _CallAndUpdateTrace(
	[rank0]: ^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/fire/core.py", line 684, in _CallAndUpdateTrace
	[rank0]: component = fn(varargs, *kwargs)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/workspace/axolotl/src/axolotl/cli/train.py", line 88, in do_cli
	[rank0]: return do_train(parsed_cfg, parsed_cli_args)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/workspace/axolotl/src/axolotl/cli/train.py", line 45, in do_train
	[rank0]: model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/workspace/axolotl/src/axolotl/telemetry/errors.py", line 124, in wrapper
	[rank0]: return func(args, *kwargs)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/workspace/axolotl/src/axolotl/train.py", line 598, in train
	[rank0]: execute_training(cfg, trainer, resume_from_checkpoint)
	[rank0]: File "/workspace/axolotl/src/axolotl/train.py", line 213, in execute_training
	[rank0]: trainer.train(resume_from_checkpoint=resume_from_checkpoint)
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer.py", line 2325, in train
	[rank0]: return inner_training_loop(
	[rank0]: ^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer.py", line 2573, in _inner_training_loop
	[rank0]: self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer_callback.py", line 506, in on_train_begin
	[rank0]: return self.call_event("on_train_begin", args, state, control)
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/trainer_callback.py", line 556, in call_event
	[rank0]: result = getattr(callback, event)(
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/integrations/integration_utils.py", line 1489, in on_train_begin
	[rank0]: self.setup(args, state, model)
	[rank0]: File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/integrations/integration_utils.py", line 1430, in setup
	[rank0]: if not self._ml_flow.is_tracking_uri_set():
	[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	[rank0]: AttributeError: module 'mlflow' has no attribute 'is_tracking_uri_set'