Instructions to use felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0")

Transformers

How to use felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 with Transformers:

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

pipe = pipeline("text-generation", model="felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0")
model = AutoModelForMultimodalLM.from_pretrained("felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0")
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 felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0"
# 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/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

SGLang

How to use felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 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/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0" \
    --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/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0",
		"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/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0" \
        --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/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0 with Docker Model Runner:
```
docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0
```

See axolotl config

axolotl version: 0.16.1

base_model: Qwen/Qwen2.5-Coder-7B-Instruct
model_type: Qwen2ForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false

# Pre-tokenized datasets produced by scripts/preprocess_diff_mask_chat.py
# (from felixwangg/prime_vul_minus_splitted which has a fixed validation split).
# Columns: input_ids, attention_mask, labels, diff_mask.
# Labels are already -100 for non-assistant tokens; axolotl keeps them as-is.
datasets:
  - path: felixwangg/prime_vul_minus_splitted_token_diff_mask_skip_indent_ctx0_chat_v2
    type: pretokenized
    split: train
test_datasets:
  - path: felixwangg/prime_vul_minus_splitted_token_diff_mask_skip_indent_ctx0_chat_v2
    type: pretokenized
    split: validation
dataset_prepared_path: /u901/t577wang/SecSteer-v2/axolotl-datasets/lora/Qwen2.5-Coder-7B/prime_vul_minus_splitted_token_diff_mask_skip_indent_ctx0_chat_v2
val_set_size: 0
output_dir: /u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir: /u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-evol-33k-stage1/checkpoint-424
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
merge_lora: false

wandb_project: diff-mask-evol-33k-primevul-ctx-0
wandb_entity: wtkuan
wandb_watch: "false"
wandb_name: Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0
wandb_log_model: "false"

gradient_accumulation_steps: 8
micro_batch_size: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 4e-05

bf16: true
tf32: false

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

num_epochs: 2
warmup_ratio: 0.1
early_stopping_patience: 1000
eval_steps: 15
save_steps: 15
save_total_limit: 1000
load_best_model_at_end: true

weight_decay: 0.02
special_tokens:

# Diff-mask weighted loss: CE(logit_t, label_t) * (1 + alpha * diff_mask_{t+1})
# Security-sensitive tokens (diff_mask=1) get weight (1 + diff_mask_alpha).
# Requires PYTHONPATH to include the repo root so diff_mask_trainer is importable.
diff_mask_alpha: 0.5

plugins:
  - diff_mask_trainer.plugin.DiffMaskPlugin
  # - sec_bench_callback.SecBenchPlugin

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the felixwangg/prime_vul_minus_splitted_token_diff_mask_skip_indent_ctx0_chat_v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.7379
Ppl: 2.0916
Memory/max Active (gib): 42.65
Memory/max Allocated (gib): 42.65
Memory/device Reserved (gib): 64.65

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: 4e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 11
training_steps: 114

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	0.8291	2.2913	42.34	42.34	51.09
6.0747	0.2632	15	0.7800	2.1815	42.65	42.65	60.59
6.3705	0.5263	30	0.7546	2.1268	42.65	42.65	64.65
5.8520	0.7895	45	0.7448	2.1060	42.65	42.65	64.65
6.1563	1.0526	60	0.7409	2.0978	42.65	42.65	64.65
5.8213	1.3158	75	0.7389	2.0936	42.65	42.65	64.65
6.5173	1.5789	90	0.7382	2.0921	42.65	42.65	64.65
5.5730	1.8421	105	0.7379	2.0915	42.65	42.65	64.65
6.4048	2.0	114	0.7379	2.0916	42.65	42.65	64.65

Framework versions

PEFT 0.19.1
Transformers 5.5.4
Pytorch 2.11.0+cu130
Datasets 4.5.0
Tokenizers 0.22.2

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Model tree for felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

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this model

felixwangg
/

Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0

Dataset used to train felixwangg/Qwen2.5-Coder-7B-evol-33k-stage1-insec-stage2-token-diff-ctx0