Instructions to use felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k 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-func-stage1-sec-stage2-4k")

Transformers

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k with Transformers:

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

pipe = pipeline("text-generation", model="felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k")
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-func-stage1-sec-stage2-4k")
model = AutoModelForMultimodalLM.from_pretrained("felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k")
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-func-stage1-sec-stage2-4k 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-func-stage1-sec-stage2-4k"
# 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-func-stage1-sec-stage2-4k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k

SGLang

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k 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-func-stage1-sec-stage2-4k" \
    --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-func-stage1-sec-stage2-4k",
		"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-func-stage1-sec-stage2-4k" \
        --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-func-stage1-sec-stage2-4k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k with Docker Model Runner:
```
docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k
```

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

datasets:
  - path: felixwangg/prime_vul_plus_splitted
    type: chat_template
    split: train
test_datasets:
  - path: felixwangg/prime_vul_plus_splitted
    type: chat_template
    split: validation
dataset_prepared_path: /u901/t577wang/SecSteer-v2/axolotl-datasets/lora/Qwen2.5-Coder-7B/func-stage1-sec-stage2-4k
val_set_size: 0
output_dir: /u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k
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-func-stage1-4k/checkpoint-54
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
merge_lora: true

wandb_project: sft-primevul-sweep-ctx-0
wandb_entity: wtkuan
wandb_watch: "false"
wandb_name: Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k
wandb_log_model: "false"


gradient_accumulation_steps: 8
micro_batch_size: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 4e-5

bf16: true
tf32: false

train_on_inputs: false
roles_to_train: ['assistant']

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

num_epochs: 1
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:

plugins:

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k

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

Loss: 0.7125
Ppl: 2.0392
Memory/max Active (gib): 38.14
Memory/max Allocated (gib): 38.14
Memory/device Reserved (gib): 52.47

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: 5
training_steps: 57

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	0.7604	2.1391	37.83	37.83	41.81
0.7392	0.2661	15	0.7265	2.0679	38.14	38.14	51.31
0.7171	0.5322	30	0.7161	2.0465	38.14	38.14	52.47
0.6792	0.7982	45	0.7129	2.0399	38.14	38.14	52.47
0.7110	1.0	57	0.7125	2.0392	38.14	38.14	52.47

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-func-stage1-sec-stage2-4k

Base model

Qwen/Qwen2.5-7B

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Qwen/Qwen2.5-Coder-7B

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

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felixwangg
/

Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k

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-func-stage1-sec-stage2-4k

Dataset used to train felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2-4k