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
```

AIGCodeGeek-DS-6.7B / README.md

Leon-Leee

Update README.md

e88bb02 verified about 2 years ago

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	---
	library_name: transformers
	tags:
	- code
	datasets:
	- Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped
	- m-a-p/Code-Feedback
	- openbmb/UltraInteract_sft
	- ise-uiuc/Magicoder-Evol-Instruct-110K
	- flytech/python-codes-25k
	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
	- Developed by: [Leon Li](https://huggingface.co/Leon-Leee)
	- License: [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL)
	- Fine-tuned from [deepseek-ai/deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) with full parameters

	### 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.

	### Requirements
	It should work with the same requirements as DeepSeek-Coder-6.7B or the following packages:

	```torch>=2.0
	tokenizers>=0.14.0
	transformers>=4.35.0
	accelerate
	sympy>=1.12
	pebble
	timeout-decorator
	attrdict
	```


	### 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
	outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
	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:
	- [DeepSeekCoder](https://huggingface.co/deepseek-ai): impressive model series and insightful tech reports
	- [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol Instruct and public datasets
	- We used a ([Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped](https://huggingface.co/datasets/Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped)) since this original has been deleted.
	- [Magicoder](https://github.com/ise-uiuc/magicoder/): OSS-Instruct, [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K) from theblackcat102/evol-codealpaca-v1(https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)
	- [Eurus](https://github.com/OpenBMB/Eurus): creative datasets for reasoning, [openbmb/UltraInteract_sft](https://huggingface.co/datasets/openbmb/UltraInteract_sft)
	- [OpenCoderInterpreter](https://opencodeinterpreter.github.io/): well-designed system and datasets [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback)
	- [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k): diversity
	- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory): easily used to finetune base models