Instructions to use BCCard/Qwen2.5-Coder-7B-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BCCard/Qwen2.5-Coder-7B-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="BCCard/Qwen2.5-Coder-7B-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BCCard/Qwen2.5-Coder-7B-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("BCCard/Qwen2.5-Coder-7B-FP8-Dynamic")
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 BCCard/Qwen2.5-Coder-7B-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BCCard/Qwen2.5-Coder-7B-FP8-Dynamic

SGLang

How to use BCCard/Qwen2.5-Coder-7B-FP8-Dynamic 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 "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic" \
    --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": "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic",
		"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 "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic" \
        --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": "BCCard/Qwen2.5-Coder-7B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BCCard/Qwen2.5-Coder-7B-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/BCCard/Qwen2.5-Coder-7B-FP8-Dynamic
```

sh2orc commited on Jun 20, 2025

Commit

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Update README.md

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@@ -17,7 +17,7 @@ tags:
 # Qwen2.5-Coder-7B-FP8-Dynamic
 ## Model Overview
-- **Model Architecture:** Qwen2.5-VL-72B-Instruct
   - **Input:** Text
   - **Output:** Text
 - **Model Optimizations:**
@@ -27,7 +27,7 @@ tags:
 - **Version:** 1.0
 - **Model Developers:** BC Card
-Quantized version of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct).
 ### Model Optimizations

 # Qwen2.5-Coder-7B-FP8-Dynamic
 ## Model Overview
+- **Model Architecture:** Qwen2.5-Coder-72B-Instruct
   - **Input:** Text
   - **Output:** Text
 - **Model Optimizations:**
 - **Version:** 1.0
 - **Model Developers:** BC Card
+Quantized version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B).
 ### Model Optimizations