Instructions to use study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4")
model = AutoModelForCausalLM.from_pretrained("study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4")
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 study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4

SGLang

How to use study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 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 "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4" \
    --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": "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4",
		"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 "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4" \
        --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": "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 with Docker Model Runner:
```
docker model run hf.co/study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4
```

study-hjt commited on Apr 26, 2024

Commit

cdfcd16

verified ·

1 Parent(s): f04d0bb

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -56,15 +56,15 @@ KeyError: 'qwen2'.
 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
 ```python
-from modelscope import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
-    "huangjintao/CodeQwen1.5-7B-Chat-GPTQ-Int4",
     torch_dtype="auto",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("huangjintao/CodeQwen1.5-7B-Chat-GPTQ-Int4")
 prompt = "Write a quicksort algorithm in python."
 messages = [

 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
+    "study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4",
     torch_dtype="auto",
     device_map="auto"
 )
+tokenizer = AutoTokenizer.from_pretrained("study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4")
 prompt = "Write a quicksort algorithm in python."
 messages = [