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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
License: DeepSeek
Fine-tuned from 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:

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

QuickStart

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: impressive model series and insightful tech reports
WizardCoder: Evol Instruct and public datasets
- We used a (Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped) since this original has been deleted.
Magicoder: OSS-Instruct, Magicoder-Evol-Instruct-110K from theblackcat102/evol-codealpaca-v1(https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)
Eurus: creative datasets for reasoning, openbmb/UltraInteract_sft
OpenCoderInterpreter: well-designed system and datasets m-a-p/Code-Feedback
flytech/python-codes-25k: diversity
LLaMA-Factory: easily used to finetune base models

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Safetensors

Model size

7B params

Tensor type

BF16

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Quantizations

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