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DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF
CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF
DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF is a machine learning model from CISCai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
- Model creator: DeepSeek AI - Original model: DeepSeek-Coder-V2-Lite-Instruct
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- License
- other
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- Public
- Created
- Jun 17, 2024
- Updated
- Jun 17, 2024
- SHA
- b0bcc4e1
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Model card
From the Hugging Face model README
DeepSeek-Coder-V2-Lite-Instruct - SOTA GGUF
- Model creator: DeepSeek AI
- Original model: DeepSeek-Coder-V2-Lite-Instruct
Description
This repo contains State Of The Art quantized GGUF format model files for DeepSeek-Coder-V2-Lite-Instruct.
Quantization was done with an importance matrix that was trained for ~250K tokens (64 batches of 4096 tokens) of answers from the CodeFeedback-Filtered-Instruction dataset.
Fill-in-Middle token metadata has been added, see example.
NOTE: Due to some of the tensors in this model being oddly shaped a consequential portion of the quantization fell back to IQ4_NL instead of the specified method, causing somewhat larger (and "smarter"; even IQ1_M is quite usable) model files than usual!
<!-- description end --> <!-- prompt-template start -->Prompt template: DeepSeek v2
User: {prompt}
Assistant:
Compatibility
These quantised GGUFv3 files are compatible with llama.cpp from May 29th 2024 onwards, as of commit fb76ec2
They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
Explanation of quantisation methods
<details> <summary>Click to see details</summary>The new methods available are:
- GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
- GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
- GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
- GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
- GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
- GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
- GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
- GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
- GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
- GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
- GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
- GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start -->Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| DeepSeek-Coder-V2-Lite-Instruct.IQ1_S.gguf | IQ1_S | 1 | 4.5 GB | 5.5 GB | smallest, significant quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ1_M.gguf | IQ1_M | 1 | 4.7 GB | 5.7 GB | very small, significant quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ2_XXS.gguf | IQ2_XXS | 2 | 5.1 GB | 6.1 GB | very small, high quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ2_XS.gguf | IQ2_XS | 2 | 5.4 GB | 6.4 GB | very small, high quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ2_S.gguf | IQ2_S | 2 | 5.4 GB | 6.4 GB | small, substantial quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ2_M.gguf | IQ2_M | 2 | 5.7 GB | 6.7 GB | small, greater quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ3_XXS.gguf | IQ3_XXS | 3 | 6.3 GB | 7.3 GB | very small, high quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ3_XS.gguf | IQ3_XS | 3 | 6.5 GB | 7.5 GB | small, substantial quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ3_S.gguf | IQ3_S | 3 | 6.8 GB | 7.8 GB | small, greater quality loss |
| DeepSeek-Coder-V2-Lite-Instruct.IQ3_M.gguf | IQ3_M | 3 | 6.9 GB | 7.9 GB | medium, balanced quality - recommended |
| DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf | IQ4_NL | 4 | 8.1 GB | 9.1 GB | small, substantial quality loss |
Generated importance matrix file: DeepSeek-Coder-V2-Lite-Instruct.imatrix.dat
Note: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-run start -->Example llama.cpp command
Make sure you are using llama.cpp from commit fb76ec3 or later.
./llama-cli -ngl 28 -m DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf --color -c 131072 --temp 0 --repeat-penalty 1.1 -p "User: {prompt}\n\nAssistant:"
Change -ngl 28 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 131072 to the desired sequence length.
If you are low on V/RAM try quantizing the K-cache with -ctk q8_0 or even -ctk q4_0 for big memory savings (depending on context size).
There is a similar option for V-cache (-ctv), however that requires Flash Attention which is not working yet with this model.
For other parameters and how to use them, please refer to the llama.cpp documentation
How to run from Python code
You can use GGUF models from Python using the llama-cpp-python module.
How to load this model in Python code, using llama-cpp-python
For full documentation, please see: llama-cpp-python docs.
First install the package
Run one of the following commands, according to your system:
# Prebuilt wheel with basic CPU support
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
# Prebuilt wheel with NVidia CUDA acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
# Prebuilt wheel with Metal GPU acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
# Build base version with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# Or with Vulkan acceleration
CMAKE_ARGS="-DLLAMA_VULKAN=on" pip install llama-cpp-python
# Or with Kompute acceleration
CMAKE_ARGS="-DLLAMA_KOMPUTE=on" pip install llama-cpp-python
# Or with SYCL acceleration
CMAKE_ARGS="-DLLAMA_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_CUDA=on"
pip install llama-cpp-python
Simple llama-cpp-python example code
from llama_cpp import Llama
# Chat Completion API
llm = Llama(model_path="./DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf", n_gpu_layers=28, n_ctx=131072)
print(llm.create_chat_completion(
repeat_penalty = 1.1,
messages = [
{
"role": "user",
"content": "Pick a LeetCode challenge and solve it in Python."
}
]
))
Simple llama-cpp-python example fill-in-middle code
from llama_cpp import Llama
# Completion API
prompt = "def add("
suffix = "\n return sum\n\n"
llm = Llama(model_path="./DeepSeek-Coder-V2-Lite-Instruct.IQ4_NL.gguf", n_gpu_layers=28, n_ctx=131072)
output = llm.create_completion(
temperature = 0.0,
repeat_penalty = 1.0,
prompt = prompt,
suffix = suffix
)
# Models sometimes repeat suffix in response, attempt to filter that
response = output["choices"][0]["text"]
response_stripped = response.rstrip()
unwanted_response_suffix = suffix.rstrip()
unwanted_response_length = len(unwanted_response_suffix)
filtered = False
if unwanted_response_suffix and response_stripped[-unwanted_response_length:] == unwanted_response_suffix:
response = response_stripped[:-unwanted_response_length]
filtered = True
print(f"Fill-in-Middle completion{' (filtered)' if filtered else ''}:\n\n{prompt}\033[32m{response}\033[{'33' if filtered else '0'}m{suffix}\033[0m")
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
1. Introduction
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from DeepSeek-Coder-V2-Base with 6 trillion tokens sourced from a high-quality and multi-source corpus. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-Coder-V2-Base, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
<p align="center"> <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true"> </p>In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found in the paper.
2. Model Downloads
We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the DeepSeekMoE framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
<div align="center">| Model | #Total Params | #Active Params | Context Length | Download |
|---|---|---|---|---|
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | 🤗 HuggingFace |
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | 🤗 HuggingFace |
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | 🤗 HuggingFace |
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | 🤗 HuggingFace |
3. Chat Website
You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: coder.deepseek.com
4. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com. Sign up for over millions of free tokens. And you can also pay-as-you-go at an unbeatable price.
<p align="center"> <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true"> </p>5. How to run locally
Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.
Inference with Huggingface's Transformers
You can directly employ Huggingface's Transformers for model inference.
Code Completion
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Code Insertion
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
Chat Completion
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", 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
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))
The complete chat template can be found within tokenizer_config.json located in the huggingface model repository.
An example of chat template is as belows:
<｜begin▁of▁sentence｜>User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
You can also add an optional system message:
<｜begin▁of▁sentence｜>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
Inference with vLLM (recommended)
To utilize vLLM for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you?"}],
[{"role": "user", "content": "write a quick sort algorithm in python."}],
[{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
6. License
This code repository is licensed under the MIT License. The use of DeepSeek-Coder-V2 Base/Instruct models is subject to the Model License. DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
7. Contact
If you have any questions, please raise an issue or contact us at [email protected].
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- Created
- Jun 17, 2024
- Updated
- Jun 17, 2024