Instructions to use WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B")
model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B")
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 WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B

SGLang

How to use WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B 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 "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B" \
    --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": "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B",
		"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 "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B" \
        --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": "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B with Docker Model Runner:
```
docker model run hf.co/WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B
```

migtissera commited on Oct 9, 2024

Commit

8a31f8a

verified ·

1 Parent(s): f958daa

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -8

README.md CHANGED Viewed

@@ -135,14 +135,12 @@ conversation = f"""<|im_start|>system\nYou are an AI that code. Please answer wi
 while True:
     user_input = input("You: ")
-    llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
     answer = generate_text(llm_prompt)
     print(answer)
-    conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
-    json_data = {"prompt": user_input, "answer": answer}
-    with open(output_file_path, "a") as output_file:
-        output_file.write(json.dumps(json_data) + "\n")
 ```

 while True:
     user_input = input("You: ")
+    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\nSure! Let me provide a complete and a thorough answer to your question, with functional and production ready code.\n"
     answer = generate_text(llm_prompt)
+    print("=" * 132)
     print(answer)
+    answer_output = extract_output(answer)
+    print("=" * 132)
+    print(answer_output)
+    conversation = f"{llm_prompt}{answer_output}<|im_end|>\n<|im_start|>user\n"
 ```