Deepseek Coder 1.3b Instruct GPTQ

🚀 Deepseek Coder 1.3B Instruct - GPTQ

This repository provides GPTQ model files for Deepseek Coder 1.3B Instruct, offering multiple quantisation options to meet various hardware and performance requirements.

📦 Installation

In text - generation - webui

To download from the main branch, enter TheBloke/deepseek - coder - 1.3b - instruct - GPTQ in the "Download model" box. To download from another branch, add :branchname to the end of the download name, e.g., TheBloke/deepseek - coder - 1.3b - instruct - GPTQ:gptq - 4bit - 32g - actorder_True

From the command line

I recommend using the huggingface - hub Python library:

pip3 install huggingface - hub

To download the main branch to a folder called deepseek - coder - 1.3b - instruct - GPTQ:

mkdir deepseek - coder - 1.3b - instruct - GPTQ
huggingface - cli download TheBloke/deepseek - coder - 1.3b - instruct - GPTQ --local - dir deepseek - coder - 1.3b - instruct - GPTQ --local - dir - use - symlinks False

To download from a different branch, add the --revision parameter:

mkdir deepseek - coder - 1.3b - instruct - GPTQ
huggingface - cli download TheBloke/deepseek - coder - 1.3b - instruct - GPTQ --revision gptq - 4bit - 32g - actorder_True --local - dir deepseek - coder - 1.3b - instruct - GPTQ --local - dir - use - symlinks False

With git (not recommended)

To clone a specific branch with git, use a command like this:

git clone --single - branch --branch gptq - 4bit - 32g - actorder_True https://huggingface.co/TheBloke/deepseek - coder - 1.3b - instruct - GPTQ

💻 Usage Examples

In [text - generation - webui](https://github.com/oobabooga/text - generation - webui)

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/deepseek - coder - 1.3b - instruct - GPTQ.
   • To download from a specific branch, enter for example TheBloke/deepseek - coder - 1.3b - instruct - GPTQ:gptq - 4bit - 32g - actorder_True
   • see Provided Files below for the list of branches for each option.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: deepseek - coder - 1.3b - instruct - GPTQ
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
   • Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.
9. Once you're ready, click the Text Generation tab and enter a prompt to get started!

Serving from Text Generation Inference (TGI)

It's recommended to use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text - generation - inference:1.1.0

Example Docker parameters:

--model - id TheBloke/deepseek - coder - 1.3b - instruct - GPTQ --port 3000 --quantize gptq --max - input - length 3696 --max - total - tokens 4096 --max - batch - prefill - tokens 4096

Example Python code for interfacing with TGI (requires huggingface - hub 0.17.0 or later):

pip3 install huggingface - hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your - endpoint - url - here"

prompt = "Tell me about AI"
prompt_template=f'''You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non - computer science questions, you will refuse to answer.
### Instruction:
{prompt}
### Response:
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens = 128,
                                  do_sample = True,
                                  temperature = 0.7,
                                  top_p = 0.95,
                                  top_k = 40,
                                  repetition_penalty = 1.1)

print(f"Model output: {response}")

From Python code

Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install transformers optimum
pip3 install auto - gptq --extra - index - url https://huggingface.github.io/autogptq - index/whl/cu118/  # Use cu117 if on CUDA 11.7

If you have problems installing AutoGPTQ using the pre - built wheels, install it from source instead:

pip3 uninstall - y auto - gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .

Use the following code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/deepseek - coder - 1.3b - instruct - GPTQ"
# To use a different branch, change revision
# For example: revision="gptq - 4bit - 32g - actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map = "auto",
                                             trust_remote_code = False,
                                             revision = "main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast = True)

prompt = "Tell me about AI"
prompt_template=f'''You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non - computer science questions, you will refuse to answer.
### Instruction:
{prompt}
### Response:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors = 'pt').input_ids.cuda()
output = model.generate(inputs = input_ids, temperature = 0.7, do_sample = True, top_p = 0.95, top_k = 40, max_new_tokens = 512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text - generation",
    model = model,
    tokenizer = tokenizer,
    max_new_tokens = 512,
    do_sample = True,
    temperature = 0.7,
    top_p = 0.95,
    top_k = 40,
    repetition_penalty = 1.1
)

print(pipe(prompt_template)[0]['generated_text'])

📚 Documentation

Description

This repo contains GPTQ model files for [DeepSeek's Deepseek Coder 1.3B Instruct](https://huggingface.co/deepseek - ai/deepseek - coder - 1.3b - instruct). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by Massed Compute.

Repositories available

• [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek - coder - 1.3b - instruct - AWQ)
• [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek - coder - 1.3b - instruct - GPTQ)
• [2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek - coder - 1.3b - instruct - GGUF)
• [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek - ai/deepseek - coder - 1.3b - instruct)

Prompt template: DeepSeek

You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non - computer science questions, you will refuse to answer.
### Instruction:
{prompt}
### Response:

Provided files, and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.

🔧 Technical Details

The files provided are tested to work with Transformers. For non - Mistral models, AutoGPTQ can also be used directly. ExLlama is compatible with Llama and Mistral models in 4 - bit. Please see the Provided Files table above for per - file compatibility. For a list of clients/servers, please see "Known compatible clients / servers" above.

📄 License

The license information can be found in the LICENSE file. The license name is deepseek.

⚠️ Important Note

Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface - hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)

💡 Usage Tip

It is strongly recommended to use the text - generation - webui one - click - installers unless you're sure you know how to make a manual install.

👥 Community

Discord

For further support, and discussions on these models and AI in general, join us at: TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team! Thanks to Clay from [gpus.llm - utils.org](llm - utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

• Patreon: https://patreon.com/TheBlokeAI
• Ko - Fi: https://ko - fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, ÈòøÊòé, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann - Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scr