Instructions to use felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 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-stage1-4k-ep2")

Transformers

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 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-stage1-4k-ep2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2")
model = AutoModelForCausalLM.from_pretrained("felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2")
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 felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 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-stage1-4k-ep2"
# 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-stage1-4k-ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2

SGLang

How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 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-stage1-4k-ep2" \
    --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-stage1-4k-ep2",
		"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-stage1-4k-ep2" \
        --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-stage1-4k-ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2 with Docker Model Runner:
```
docker model run hf.co/felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2
```

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/codenet-c-cpp-stage1-4k
    type: chat_template
    split: train
test_datasets:
  - path: felixwangg/codenet-c-cpp-stage1-4k
    type: chat_template
    split: validation
dataset_prepared_path: /u901/t577wang/SecSteer-v2/axolotl-datasets/lora/Qwen2.5-Coder-7B/func-stage1-4k
val_set_size: 0
output_dir: /u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-4k-ep2
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
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-stage1-4k
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: 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:

plugins:

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-4k-ep2

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the felixwangg/codenet-c-cpp-stage1-4k dataset. It achieves the following results on the evaluation set:

Loss: 0.5481
Ppl: 1.7299
Memory/max Active (gib): 38.14
Memory/max Allocated (gib): 38.14
Memory/device Reserved (gib): 54.2

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: 10
training_steps: 107

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	0.6374	1.8916	37.83	37.83	41.81
0.5964	0.2804	15	0.6105	1.8413	38.14	38.14	54.2
0.5270	0.5607	30	0.5679	1.7646	38.14	38.14	54.2
0.5420	0.8411	45	0.5589	1.7487	38.14	38.14	54.2
0.4953	1.1121	60	0.5538	1.7398	38.14	38.14	54.2
0.5211	1.3925	75	0.5504	1.7339	38.14	38.14	54.2
0.5713	1.6729	90	0.5484	1.7305	38.14	38.14	54.21
0.5435	1.9533	105	0.5481	1.7300	38.14	38.14	54.21
0.5058	1.9907	107	0.5481	1.7299	38.14	38.14	54.2

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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Qwen2.5-Coder-7B-func-stage1-4k-ep2

u901/t577wang/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-4k-ep2

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-stage1-4k-ep2

Dataset used to train felixwangg/Qwen2.5-Coder-7B-func-stage1-4k-ep2