Instructions to use kechengcode/Qwen2-5B-Instruct-16Layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kechengcode/Qwen2-5B-Instruct-16Layers with Transformers:

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

pipe = pipeline("text-generation", model="kechengcode/Qwen2-5B-Instruct-16Layers")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kechengcode/Qwen2-5B-Instruct-16Layers")
model = AutoModelForCausalLM.from_pretrained("kechengcode/Qwen2-5B-Instruct-16Layers")
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 kechengcode/Qwen2-5B-Instruct-16Layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kechengcode/Qwen2-5B-Instruct-16Layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kechengcode/Qwen2-5B-Instruct-16Layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kechengcode/Qwen2-5B-Instruct-16Layers

SGLang

How to use kechengcode/Qwen2-5B-Instruct-16Layers 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 "kechengcode/Qwen2-5B-Instruct-16Layers" \
    --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": "kechengcode/Qwen2-5B-Instruct-16Layers",
		"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 "kechengcode/Qwen2-5B-Instruct-16Layers" \
        --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": "kechengcode/Qwen2-5B-Instruct-16Layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kechengcode/Qwen2-5B-Instruct-16Layers with Docker Model Runner:
```
docker model run hf.co/kechengcode/Qwen2-5B-Instruct-16Layers
```

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Qwen/Qwen2-7B-Instruct

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 6]
    model: Qwen/Qwen2-7B-Instruct
- sources:
  - layer_range: [18, 28]
    model: Qwen/Qwen2-7B-Instruct

Downloads last month: 5

Safetensors

Model size

5B params

Tensor type

F16

Model tree for kechengcode/Qwen2-5B-Instruct-16Layers

Base model

Qwen/Qwen2-7B

Finetuned

Qwen/Qwen2-7B-Instruct

Finetuned

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