Instructions to use lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6 with Transformers:

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

pipe = pipeline("text-generation", model="lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6")
model = AutoModelForCausalLM.from_pretrained("lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6")
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 lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6

SGLang

How to use lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6 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 "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6" \
    --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": "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6",
		"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 "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6" \
        --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": "lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6 with Docker Model Runner:
```
docker model run hf.co/lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6
```

2and3_apps_30k_v6

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the 2and3_apps_30k_v6 dataset. It achieves the following results on the evaluation set:

Loss: 0.1605

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: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 8
total_eval_batch_size: 4
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.1745	0.1025	100	0.1826
0.1915	0.2049	200	0.1744
0.1889	0.3074	300	0.1699
0.203	0.4098	400	0.1674
0.1834	0.5123	500	0.1656
0.1807	0.6148	600	0.1633
0.1853	0.7172	700	0.1625
0.173	0.8197	800	0.1614
0.1793	0.9221	900	0.1606

Framework versions

Transformers 4.46.1
Pytorch 2.6.0+cu124
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for lemonhat/Qwen2.5-Coder-7B-Instruct-2and3_apps_30k_v6

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