Instructions to use aigcode/AIGCodeGeek-DS-6.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aigcode/AIGCodeGeek-DS-6.7B with Transformers:

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

pipe = pipeline("text-generation", model="aigcode/AIGCodeGeek-DS-6.7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B")
model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B")
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

vLLM

How to use aigcode/AIGCodeGeek-DS-6.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aigcode/AIGCodeGeek-DS-6.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aigcode/AIGCodeGeek-DS-6.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aigcode/AIGCodeGeek-DS-6.7B

SGLang

How to use aigcode/AIGCodeGeek-DS-6.7B 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 "aigcode/AIGCodeGeek-DS-6.7B" \
    --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": "aigcode/AIGCodeGeek-DS-6.7B",
		"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 "aigcode/AIGCodeGeek-DS-6.7B" \
        --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": "aigcode/AIGCodeGeek-DS-6.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aigcode/AIGCodeGeek-DS-6.7B with Docker Model Runner:
```
docker model run hf.co/aigcode/AIGCodeGeek-DS-6.7B
```

Leon-Leee commited on Apr 29, 2024

Commit

e88bb02

verified ·

1 Parent(s): 517a948

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -1,5 +1,4 @@
 ---
-license name: deepseek
 library_name: transformers
 tags:
 - code
@@ -12,11 +11,13 @@ datasets:
 metrics:
 - code_eval
 pipeline_tag: text-generation
 ---
 ## AIGCodeGeek-DS-6.7B
 ### Introduction
-AIGCodeGeek-DS-6.7B is the first released version of our Code-LLM family with competitive performance on public and private benchmarks.
 ### Model Details
 #### Model Description
@@ -26,7 +27,7 @@ AIGCodeGeek-DS-6.7B is the first released version of our Code-LLM family with co
 ### Training data
 A mixture of samples from high-quality open-source (read *Acknowledgements*) and our private datasets.
-We have made contamination detection as Magicoder/Bigcode did.
 ### Evaluation
 results to be added.
@@ -48,11 +49,12 @@ attrdict
 ### QuickStart
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 messages=[
-    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
 ]
 inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
 # tokenizer.eos_token_id is the id of <|EOT|> token
@@ -60,8 +62,6 @@ outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50,
 print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
 ```
-### Limits
 ### Acknowledgements
 We gain a lot of knowledge and resources from the open-source community:

 ---
 library_name: transformers
 tags:
 - code
 metrics:
 - code_eval
 pipeline_tag: text-generation
+license: other
+license name: deepseek
 ---
 ## AIGCodeGeek-DS-6.7B
 ### Introduction
+AIGCodeGeek-DS-6.7B is our first released version of a Code-LLM family with competitive performance on public and private benchmarks.
 ### Model Details
 #### Model Description
 ### Training data
 A mixture of samples from high-quality open-source (read *Acknowledgements*) and our private datasets.
+We have made contamination detection as Magicoder/Bigcode did (https://github.com/ise-uiuc/magicoder/blob/main/src/magicoder/decontamination/find_substrings.py).
 ### Evaluation
 results to be added.
 ### QuickStart
 ```python
+import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 messages=[
+    { 'role': 'user', 'content': "write a merge sort algorithm in python."}
 ]
 inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
 # tokenizer.eos_token_id is the id of <|EOT|> token
 print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
 ```
 ### Acknowledgements
 We gain a lot of knowledge and resources from the open-source community: