Instructions to use Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2

SGLang

How to use Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 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 "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/Volko76/Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2
```

Qwen2.5-Coder-0.5B-Instruct-8.0bpw-exl2 / base_model /config.json

Volko76

Upload folder using huggingface_hub

c9740d7 verified almost 2 years ago

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	{
	"architectures": [
	"Qwen2ForCausalLM"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"eos_token_id": 151645,
	"hidden_act": "silu",
	"hidden_size": 896,
	"initializer_range": 0.02,
	"intermediate_size": 4864,
	"max_position_embeddings": 32768,
	"max_window_layers": 21,
	"model_type": "qwen2",
	"num_attention_heads": 14,
	"num_hidden_layers": 24,
	"num_key_value_heads": 2,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"sliding_window": 32768,
	"tie_word_embeddings": true,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.43.1",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 151936
	}