Instructions to use felixwangg/Qwen3.5-9B-cot-sec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixwangg/Qwen3.5-9B-cot-sec with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "felixwangg/Qwen3.5-9B-cot-sec")

Transformers

How to use felixwangg/Qwen3.5-9B-cot-sec with Transformers:

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

pipe = pipeline("text-generation", model="felixwangg/Qwen3.5-9B-cot-sec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("felixwangg/Qwen3.5-9B-cot-sec")
model = AutoModelForImageTextToText.from_pretrained("felixwangg/Qwen3.5-9B-cot-sec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use felixwangg/Qwen3.5-9B-cot-sec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "felixwangg/Qwen3.5-9B-cot-sec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixwangg/Qwen3.5-9B-cot-sec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixwangg/Qwen3.5-9B-cot-sec

SGLang

How to use felixwangg/Qwen3.5-9B-cot-sec 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 "felixwangg/Qwen3.5-9B-cot-sec" \
    --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": "felixwangg/Qwen3.5-9B-cot-sec",
		"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 "felixwangg/Qwen3.5-9B-cot-sec" \
        --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": "felixwangg/Qwen3.5-9B-cot-sec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixwangg/Qwen3.5-9B-cot-sec with Docker Model Runner:
```
docker model run hf.co/felixwangg/Qwen3.5-9B-cot-sec
```

Qwen3.5-9B-cot-sec / debug.log

felixwangg

Upload folder using huggingface_hub

533ce4e verified about 2 months ago

raw

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18.8 kB

	[2026-04-17 00:05:12,906] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:3789701] baseline 0.000GB ()
	[2026-04-17 00:05:12,906] [INFO] [axolotl.cli.config.load_cfg:341] [PID:3789701] config:
	{
	"activation_offloading": false,
	"adapter": "lora",
	"axolotl_config_path": "./axolotl_configs/Qwen3.5-9B/cot-lora-primevul-plus.yaml",
	"base_model": "Qwen/Qwen3.5-9B",
	"base_model_config": "Qwen/Qwen3.5-9B",
	"batch_size": 32,
	"bf16": true,
	"capabilities": {
	"bf16": true,
	"compute_capability": "sm_90",
	"fp8": true,
	"n_gpu": 1,
	"n_node": 1,
	"tf32": true
	},
	"context_parallel_size": 1,
	"dataloader_num_workers": 1,
	"dataloader_pin_memory": true,
	"dataloader_prefetch_factor": 256,
	"dataset_num_proc": 16,
	"dataset_prepared_path": "/home/tkwang/scratch/SecSteer-v2/axolotl-datasets/lora/Qwen3.5-9B/cot-sec",
	"datasets": [
	{
	"chat_template": "tokenizer_default",
	"message_property_mappings": {
	"content": "content",
	"role": "role"
	},
	"path": "felixwangg/glm-4.6v-distilled-sec-cot",
	"split": "train",
	"trust_remote_code": false,
	"type": "chat_template"
	}
	],
	"ddp": false,
	"ddp_find_unused_parameters": true,
	"device": "cuda:0",
	"device_map": "auto",
	"dion_rank_fraction": 1.0,
	"dion_rank_multiple_of": 1,
	"eaft_alpha": 1.0,
	"eaft_k": 20,
	"early_stopping_patience": 1000,
	"env_capabilities": {
	"torch_version": "2.11.0"
	},
	"eval_batch_size": 4,
	"eval_causal_lm_metrics": [
	"sacrebleu",
	"comet",
	"ter",
	"chrf"
	],
	"eval_max_new_tokens": 128,
	"eval_sample_packing": false,
	"eval_steps": 15,
	"eval_table_size": 0,
	"experimental_skip_move_to_device": true,
	"flash_attention": true,
	"fp16": false,
	"generate_samples": false,
	"generation_do_sample": true,
	"generation_max_new_tokens": 50,
	"generation_prompt_ratio": 0.5,
	"generation_temperature": 0.7,
	"gradient_accumulation_steps": 8,
	"gradient_checkpointing": true,
	"gradient_checkpointing_kwargs": {
	"use_reentrant": false
	},
	"include_tkps": true,
	"is_falcon_derived_model": false,
	"is_llama_derived_model": false,
	"is_mistral_derived_model": false,
	"is_multimodal": true,
	"layer_offloading": false,
	"learning_rate": 4e-05,
	"lisa_layers_attribute": "model.layers",
	"load_best_model_at_end": true,
	"load_in_4bit": false,
	"load_in_8bit": false,
	"local_rank": 0,
	"logging_steps": 1,
	"lora_alpha": 16,
	"lora_dropout": 0.05,
	"lora_embedding_kernel": true,
	"lora_mlp_kernel": true,
	"lora_model_dir": "axolotl-outputs/lora/Qwen3.5-9B-cot-sec",
	"lora_o_kernel": true,
	"lora_qkv_kernel": true,
	"lora_r": 16,
	"lora_target_linear": true,
	"loraplus_lr_embedding": 1e-06,
	"lr_scheduler": "cosine",
	"mean_resizing_embeddings": false,
	"merge_lora": true,
	"merge_method": "memory_efficient",
	"micro_batch_size": 4,
	"model_config_type": "qwen3_5",
	"model_config_type_text": "qwen3_5_text",
	"num_epochs": 1.0,
	"num_generation_samples": 3,
	"optimizer": "adamw_torch",
	"otel_metrics_host": "localhost",
	"otel_metrics_port": 8000,
	"output_dir": "/home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-sec",
	"pad_to_sequence_len": true,
	"pretrain_multipack_attn": true,
	"processor_config": "Qwen/Qwen3.5-9B",
	"profiler_steps_start": 0,
	"qlora_sharded_model_loading": false,
	"quantize_moe_experts": false,
	"ray_num_workers": 1,
	"resources_per_worker": {
	"GPU": 1
	},
	"sample_packing": false,
	"sample_packing_bin_size": 200,
	"sample_packing_group_size": 100000,
	"save_only_model": false,
	"save_safetensors": true,
	"save_steps": 15,
	"save_total_limit": 1000,
	"sequence_len": 4096,
	"shuffle_before_merging_datasets": false,
	"shuffle_merged_datasets": true,
	"skip_prepare_dataset": false,
	"streaming_multipack_buffer_size": 10000,
	"strict": false,
	"tensor_parallel_size": 1,
	"test_datasets": [
	{
	"chat_template": "tokenizer_default",
	"message_property_mappings": {
	"content": "content",
	"role": "role"
	},
	"path": "felixwangg/glm-4.6v-distilled-sec-cot",
	"split": "validation",
	"trust_remote_code": false,
	"type": "chat_template"
	}
	],
	"tf32": false,
	"tiled_mlp_use_original_mlp": true,
	"tokenizer_config": "Qwen/Qwen3.5-9B",
	"tokenizer_save_jinja_files": true,
	"tokenizer_type": "AutoTokenizer",
	"torch_dtype": "torch.bfloat16",
	"train_on_inputs": false,
	"trl": {
	"async_prefetch": false,
	"log_completions": false,
	"mask_truncated_completions": false,
	"ref_model_mixup_alpha": 0.9,
	"ref_model_sync_steps": 64,
	"replay_buffer_size": 0,
	"replay_recompute_logps": true,
	"reroll_max_groups": 1,
	"reroll_start_fraction": 1.0,
	"reward_num_workers": 1,
	"scale_rewards": true,
	"skip_zero_advantage_batches": true,
	"sync_ref_model": false,
	"use_data_producer": false,
	"use_vllm": false,
	"vllm_lora_sync": false,
	"vllm_server_host": "0.0.0.0",
	"vllm_server_port": 8000
	},
	"type_of_model": "AutoModelForCausalLM",
	"use_otel_metrics": false,
	"use_ray": false,
	"use_wandb": true,
	"val_set_size": 0.0,
	"vllm": {
	"device": "auto",
	"dtype": "auto",
	"gpu_memory_utilization": 0.9,
	"host": "0.0.0.0",
	"port": 8000
	},
	"wandb_entity": "wtkuan",
	"wandb_log_model": "false",
	"wandb_name": "Qwen3.5-9B-cot-sec",
	"wandb_project": "cot-qwen3.5-primevul",
	"wandb_watch": "false",
	"warmup_ratio": 0.1,
	"weight_decay": 0.02,
	"world_size": 1
	}
	[2026-04-17 00:05:12,909] [WARNING] [axolotl.utils.schemas.config.default_dataset_num_proc:1745] [PID:3789701] dataset_processes is deprecated and will be removed in a future version. Please use dataset_num_proc instead.
	[2026-04-17 00:05:12,909] [WARNING] [axolotl.utils.schemas.config.check_auto_enable_lora_kernels:1597] [PID:3789701] Auto-enabling LoRA kernel optimizations for faster training. Please explicitly set `lora_*_kernel` config values to `false` to disable. See https://docs.axolotl.ai/docs/lora_optims.html for more info.
	[2026-04-17 00:05:13,014] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:3789701] baseline 0.000GB ()
	[2026-04-17 00:05:13,015] [INFO] [axolotl.cli.config.load_cfg:341] [PID:3789701] config:
	{
	"activation_offloading": false,
	"adapter": "lora",
	"axolotl_config_path": "./axolotl_configs/Qwen3.5-9B/cot-lora-primevul-plus.yaml",
	"base_model": "Qwen/Qwen3.5-9B",
	"base_model_config": "Qwen/Qwen3.5-9B",
	"batch_size": 32,
	"bf16": true,
	"capabilities": {
	"bf16": true,
	"compute_capability": "sm_90",
	"fp8": true,
	"n_gpu": 1,
	"n_node": 1,
	"tf32": true
	},
	"context_parallel_size": 1,
	"dataloader_num_workers": 1,
	"dataloader_pin_memory": true,
	"dataloader_prefetch_factor": 256,
	"dataset_num_proc": 16,
	"dataset_prepared_path": "/home/tkwang/scratch/SecSteer-v2/axolotl-datasets/lora/Qwen3.5-9B/cot-sec",
	"datasets": [
	{
	"chat_template": "tokenizer_default",
	"message_property_mappings": {
	"content": "content",
	"role": "role"
	},
	"path": "felixwangg/glm-4.6v-distilled-sec-cot",
	"split": "train",
	"trust_remote_code": false,
	"type": "chat_template"
	}
	],
	"ddp": false,
	"ddp_find_unused_parameters": true,
	"device": "cuda:0",
	"device_map": "auto",
	"dion_rank_fraction": 1.0,
	"dion_rank_multiple_of": 1,
	"eaft_alpha": 1.0,
	"eaft_k": 20,
	"early_stopping_patience": 1000,
	"env_capabilities": {
	"torch_version": "2.11.0"
	},
	"eval_batch_size": 4,
	"eval_causal_lm_metrics": [
	"sacrebleu",
	"comet",
	"ter",
	"chrf"
	],
	"eval_max_new_tokens": 128,
	"eval_sample_packing": false,
	"eval_steps": 15,
	"eval_table_size": 0,
	"experimental_skip_move_to_device": true,
	"flash_attention": false,
	"fp16": false,
	"generate_samples": false,
	"generation_do_sample": true,
	"generation_max_new_tokens": 50,
	"generation_prompt_ratio": 0.5,
	"generation_temperature": 0.7,
	"gradient_accumulation_steps": 8,
	"gradient_checkpointing": true,
	"gradient_checkpointing_kwargs": {
	"use_reentrant": false
	},
	"include_tkps": true,
	"is_falcon_derived_model": false,
	"is_llama_derived_model": false,
	"is_mistral_derived_model": false,
	"is_multimodal": true,
	"layer_offloading": false,
	"learning_rate": 4e-05,
	"lisa_layers_attribute": "model.layers",
	"load_best_model_at_end": true,
	"load_in_4bit": false,
	"load_in_8bit": false,
	"local_rank": 0,
	"logging_steps": 1,
	"lora_alpha": 16,
	"lora_dropout": 0.05,
	"lora_embedding_kernel": true,
	"lora_mlp_kernel": true,
	"lora_model_dir": "axolotl-outputs/lora/Qwen3.5-9B-cot-sec",
	"lora_o_kernel": true,
	"lora_qkv_kernel": true,
	"lora_r": 16,
	"lora_target_linear": true,
	"loraplus_lr_embedding": 1e-06,
	"lr_scheduler": "cosine",
	"mean_resizing_embeddings": false,
	"merge_lora": true,
	"merge_method": "memory_efficient",
	"micro_batch_size": 4,
	"model_config_type": "qwen3_5",
	"model_config_type_text": "qwen3_5_text",
	"num_epochs": 1.0,
	"num_generation_samples": 3,
	"optimizer": "adamw_torch",
	"otel_metrics_host": "localhost",
	"otel_metrics_port": 8000,
	"output_dir": "/home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-sec",
	"pad_to_sequence_len": true,
	"pretrain_multipack_attn": true,
	"processor_config": "Qwen/Qwen3.5-9B",
	"profiler_steps_start": 0,
	"qlora_sharded_model_loading": false,
	"quantize_moe_experts": false,
	"ray_num_workers": 1,
	"resources_per_worker": {
	"GPU": 1
	},
	"sample_packing": false,
	"sample_packing_bin_size": 200,
	"sample_packing_group_size": 100000,
	"save_only_model": false,
	"save_safetensors": true,
	"save_steps": 15,
	"save_total_limit": 1000,
	"sequence_len": 4096,
	"shuffle_before_merging_datasets": false,
	"shuffle_merged_datasets": true,
	"skip_prepare_dataset": false,
	"streaming_multipack_buffer_size": 10000,
	"strict": false,
	"tensor_parallel_size": 1,
	"test_datasets": [
	{
	"chat_template": "tokenizer_default",
	"message_property_mappings": {
	"content": "content",
	"role": "role"
	},
	"path": "felixwangg/glm-4.6v-distilled-sec-cot",
	"split": "validation",
	"trust_remote_code": false,
	"type": "chat_template"
	}
	],
	"tf32": false,
	"tiled_mlp_use_original_mlp": true,
	"tokenizer_config": "Qwen/Qwen3.5-9B",
	"tokenizer_save_jinja_files": true,
	"tokenizer_type": "AutoTokenizer",
	"torch_dtype": "torch.bfloat16",
	"train_on_inputs": false,
	"trl": {
	"async_prefetch": false,
	"log_completions": false,
	"mask_truncated_completions": false,
	"ref_model_mixup_alpha": 0.9,
	"ref_model_sync_steps": 64,
	"replay_buffer_size": 0,
	"replay_recompute_logps": true,
	"reroll_max_groups": 1,
	"reroll_start_fraction": 1.0,
	"reward_num_workers": 1,
	"scale_rewards": true,
	"skip_zero_advantage_batches": true,
	"sync_ref_model": false,
	"use_data_producer": false,
	"use_vllm": false,
	"vllm_lora_sync": false,
	"vllm_server_host": "0.0.0.0",
	"vllm_server_port": 8000
	},
	"type_of_model": "AutoModelForCausalLM",
	"use_otel_metrics": false,
	"use_ray": false,
	"use_wandb": true,
	"val_set_size": 0.0,
	"vllm": {
	"device": "auto",
	"dtype": "auto",
	"gpu_memory_utilization": 0.9,
	"host": "0.0.0.0",
	"port": 8000
	},
	"wandb_entity": "wtkuan",
	"wandb_log_model": "false",
	"wandb_name": "Qwen3.5-9B-cot-sec",
	"wandb_project": "cot-qwen3.5-primevul",
	"wandb_watch": "false",
	"warmup_ratio": 0.1,
	"weight_decay": 0.02,
	"world_size": 1
	}
	[2026-04-17 00:05:13,015] [DEBUG] [axolotl.cli.merge_lora.do_merge_lora:32] [PID:3789701] Using memory-efficient LoRA merging method...
	[2026-04-17 00:05:13,015] [DEBUG] [axolotl.cli.merge_lora._do_merge_lora_efficient:79] [PID:3789701] Using memory-efficient LoRA merging method...
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	[2026-04-17 00:05:13,244] [DEBUG] [axolotl.cli.utils.lora_merge.merge_lora_sharded_efficient:838] [PID:3789701] LoRA scale factor: 1.0 (rslora=False)
	[2026-04-17 00:05:13,245] [DEBUG] [axolotl.cli.utils.lora_merge.merge_lora_sharded_efficient:854] [PID:3789701] Loading LoRA weights from axolotl-outputs/lora/Qwen3.5-9B-cot-sec/adapter_model.safetensors
	[2026-04-17 00:05:13,849] [DEBUG] [axolotl.cli.utils.lora_merge.merge_lora_sharded_efficient:860] [PID:3789701] Keeping LoRA weights on CPU; will move per-tensor during merge
	[2026-04-17 00:05:13,850] [DEBUG] [axolotl.cli.utils.lora_merge.merge_lora_sharded_efficient:866] [PID:3789701] Found 4 model shards in /scratch/tkwang/hf_cache/hub/models--Qwen--Qwen3.5-9B/snapshots/c202236235762e1c871ad0ccb60c8ee5ba337b9a
	[2026-04-17 00:05:13,850] [INFO] [axolotl.cli.utils.lora_merge.copy_non_model_files:303] [PID:3789701] Copying non-model files to output directory...
	[2026-04-17 00:05:13,851] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying .gitattributes to output
	[2026-04-17 00:05:13,862] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying LICENSE to output
	[2026-04-17 00:05:13,871] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying README.md to output
	[2026-04-17 00:05:13,879] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying chat_template.jinja to output
	[2026-04-17 00:05:13,888] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying config.json to output
	[2026-04-17 00:05:13,895] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying merges.txt to output
	[2026-04-17 00:05:13,907] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying preprocessor_config.json to output
	[2026-04-17 00:05:13,916] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying tokenizer.json to output
	[2026-04-17 00:05:13,942] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying tokenizer_config.json to output
	[2026-04-17 00:05:13,948] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying video_preprocessor_config.json to output
	[2026-04-17 00:05:13,955] [DEBUG] [axolotl.cli.utils.lora_merge.copy_non_model_files:324] [PID:3789701] Copying vocab.json to output
	Merging shards: 0%\| \| 0/4 [00:00<?, ?it/s][2026-04-17 00:05:13,974] [DEBUG] [axolotl.cli.utils.lora_merge._merge_tensor_with_lora:411] [PID:3789701] Merging LoRA for model.language_model.layers.14.mlp.down_proj.weight: torch.Size([16, 12288]), torch.Size([4096, 16])
	Merging shards: 0%\| \| 0/4 [00:00<?, ?it/s]
	Traceback (most recent call last):
	File "/scratch/tkwang/SecSteer-v2/.venv/bin/axolotl", line 10, in <module>
	sys.exit(main())
	^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/main.py", line 347, in main
	cli()
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/click/core.py", line 1485, in __call__
	return self.main(args, *kwargs)
	^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/click/core.py", line 1406, in main
	rv = self.invoke(ctx)
	^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/click/core.py", line 1873, in invoke
	return _process_result(sub_ctx.command.invoke(sub_ctx))
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/click/core.py", line 1269, in invoke
	return ctx.invoke(self.callback, **ctx.params)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/click/core.py", line 824, in invoke
	return callback(args, *kwargs)
	^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/utils/args.py", line 48, in wrapper
	return func(args, *filtered_kwargs)
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/main.py", line 293, in merge_lora
	do_cli(config=config, **kwargs)
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/merge_lora.py", line 169, in do_cli
	do_merge_lora(cfg=parsed_cfg)
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/telemetry/errors.py", line 124, in wrapper
	return func(args, *kwargs)
	^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/merge_lora.py", line 33, in do_merge_lora
	_do_merge_lora_efficient(cfg=cfg)
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/merge_lora.py", line 108, in _do_merge_lora_efficient
	merge_lora_sharded_efficient(
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/utils/lora_merge.py", line 916, in merge_lora_sharded_efficient
	merged_tensor, was_merged = _merge_tensor_with_lora(
	^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/utils/lora_merge.py", line 429, in _merge_tensor_with_lora
	delta = _build_peft_layer_and_get_delta(
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
	File "/scratch/tkwang/SecSteer-v2/.venv/lib/python3.12/site-packages/axolotl/cli/utils/lora_merge.py", line 254, in _build_peft_layer_and_get_delta
	layer = LoraLinear(
	^^^^^^^^^^^
	TypeError: Linear.__init__() missing 1 required positional argument: 'config'