Instructions to use TechxGenus/deepseek-coder-7b-base-v1.5-AQLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/deepseek-coder-7b-base-v1.5-AQLM with Transformers:

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

pipe = pipeline("text-generation", model="TechxGenus/deepseek-coder-7b-base-v1.5-AQLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/deepseek-coder-7b-base-v1.5-AQLM")
model = AutoModelForCausalLM.from_pretrained("TechxGenus/deepseek-coder-7b-base-v1.5-AQLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TechxGenus/deepseek-coder-7b-base-v1.5-AQLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TechxGenus/deepseek-coder-7b-base-v1.5-AQLM

SGLang

How to use TechxGenus/deepseek-coder-7b-base-v1.5-AQLM 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 "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-7b-base-v1.5-AQLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TechxGenus/deepseek-coder-7b-base-v1.5-AQLM with Docker Model Runner:
```
docker model run hf.co/TechxGenus/deepseek-coder-7b-base-v1.5-AQLM
```

[🏠Homepage] | [🤖 Chat with DeepSeek Coder] | [Discord] | [Wechat(微信)]

AQLM quantized version of deepseek-coder-6.7b-base model. Refer to the official GitHub repo for more information.

1. Introduction of Deepseek-Coder-7B-Base-v1.5

Deepseek-Coder-7B-Base-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective.

Home Page: DeepSeek
Repository: deepseek-ai/deepseek-coder
Chat With DeepSeek Coder: DeepSeek-Coder

2. Evaluation Results

3. How to Use

Here give an example of how to use our model.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-base-v1.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-base-v1.5", trust_remote_code=True).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

4. License

This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.

See the LICENSE-MODEL for more details.

5. Contact

If you have any questions, please raise an issue or contact us at [email protected].

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Paper for TechxGenus/deepseek-coder-7b-base-v1.5-AQLM

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