Instructions to use dyyyyyyyy/Qwen2-Math-7B-ScaleQuest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dyyyyyyyy/Qwen2-Math-7B-ScaleQuest with Transformers:

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

pipe = pipeline("text-generation", model="dyyyyyyyy/Qwen2-Math-7B-ScaleQuest", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/Qwen2-Math-7B-ScaleQuest")
model = AutoModelForCausalLM.from_pretrained("dyyyyyyyy/Qwen2-Math-7B-ScaleQuest", device_map="auto")
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 dyyyyyyyy/Qwen2-Math-7B-ScaleQuest with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dyyyyyyyy/Qwen2-Math-7B-ScaleQuest

SGLang

How to use dyyyyyyyy/Qwen2-Math-7B-ScaleQuest 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 "dyyyyyyyy/Qwen2-Math-7B-ScaleQuest" \
    --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": "dyyyyyyyy/Qwen2-Math-7B-ScaleQuest",
		"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 "dyyyyyyyy/Qwen2-Math-7B-ScaleQuest" \
        --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": "dyyyyyyyy/Qwen2-Math-7B-ScaleQuest",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dyyyyyyyy/Qwen2-Math-7B-ScaleQuest with Docker Model Runner:
```
docker model run hf.co/dyyyyyyyy/Qwen2-Math-7B-ScaleQuest
```

dyyyyyyyy commited on Oct 25, 2024

Commit

35e7866

verified ·

1 Parent(s): 6932bd2

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -59,14 +59,14 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 question = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
-sys_prompt="<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
-query_prompt="<|im_start|>user" + "\n"
 # {query}
-prompt_after_query="<|im_end|>" + "\n"
-resp_prompt="<|im_start|>assistant" + "\n"
-prompt_before_resp=""
 # {resp}
-delim="<|im_end|>" + "\n"
 prefix_prompt = f"{query_prompt}{question}{prompt_after_query}{resp_prompt}{prompt_before_resp}".rstrip(" ")
 full_prompt = sys_prompt + delim.join([prefix_prompt])

 question = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
+sys_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+query_prompt = "<|im_start|>user" + "\n"
 # {query}
+prompt_after_query = "\n" + "Please reason step by step, and put your final answer within \\boxed{}.<|im_end|>" + "\n"
+resp_prompt = "<|im_start|>assistant" + "\n"
+prompt_before_resp = ""
 # {resp}
+delim = "<|im_end|>" + "\n"
 prefix_prompt = f"{query_prompt}{question}{prompt_after_query}{resp_prompt}{prompt_before_resp}".rstrip(" ")
 full_prompt = sys_prompt + delim.join([prefix_prompt])