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README.md

@@ -1,5 +1,425 @@
----
-license: other
-license_name: deepseek-license
-license_link: https://github.com/deepseek-ai/DeepSeek-Coder-V2/raw/main/LICENSE-MODEL
----
+---
+license: other
+license_name: deepseek-license
+license_link: https://github.com/deepseek-ai/DeepSeek-Coder-V2/raw/main/LICENSE-MODEL
+tags:
+- code
+language:
+- code
+base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
+model_creator: DeepSeek AI
+model_name: DeepSeek-Coder-V2-Lite-Instruct
+model_type: deepseek2
+datasets:
+- m-a-p/CodeFeedback-Filtered-Instruction
+quantized_by: CISC
+---
+
+# DeepSeek-Coder-V2-Lite-Instruct - SOTA GGUF
+- Model creator: [DeepSeek AI](https://huggingface.co/deepseek-ai)
+- Original model: [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)
+
+<!-- description start -->
+## Description
+
+This repo contains State Of The Art quantized GGUF format model files for [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/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](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset.
+
+Fill-in-Middle token metadata has been added, see [example](#simple-llama-cpp-python-example-fill-in-middle-code).
+
+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:
+```
+
+<!-- prompt-template end -->
+
+
+<!-- compatibility_gguf start -->
+## Compatibility
+
+These quantised GGUFv3 files are compatible with llama.cpp from May 29th 2024 onwards, as of commit [fb76ec2](https://github.com/ggerganov/llama.cpp/commit/fb76ec31a9914b7761c1727303ab30380fd4f05c)
+
+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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct.IQ2_M.gguf) | IQ2_M | 2 | 5.7 GB| 5.7 GB | small, greater quality loss |
+| [DeepSeek-Coder-V2-Lite-Instruct.IQ3_XXS.gguf](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF/blob/main/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](https://github.com/ggerganov/llama.cpp/commit/fb76ec31a9914b7761c1727303ab30380fd4f05c) or later.
+
+```shell
+./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](https://github.com/ggerganov/llama.cpp/issues/7343).
+
+For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+## How to run from Python code
+
+You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/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](https://llama-cpp-python.readthedocs.io/en/latest/).
+
+#### First install the package
+
+Run one of the following commands, according to your system:
+
+```shell
+# 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
+
+```python
+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
+
+```python
+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")
+```
+
+<!-- README_GGUF.md-how-to-run end -->
+
+<!-- original-model-card start -->
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
+    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
+    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<p align="center">
+  <a href="#4-api-platform">API Platform</a> |
+  <a href="#5-how-to-run-locally">How to Use</a> |
+  <a href="#6-license">License</a> |
+</p>
+
+
+<p align="center">
+  <a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a>
+</p>
+
+# 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](https://arxiv.org/pdf/2401.06066) 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](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
+| DeepSeek-Coder-V2-Lite-Instruct |        16B        |        2.4B        |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
+|     DeepSeek-Coder-V2-Base      |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
+|   DeepSeek-Coder-V2-Instruct    |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
+
+</div>
+
+
+## 3. Chat Website
+
+You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
+
+## 4. API Platform
+We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://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](https://github.com/huggingface/transformers) for model inference.
+
+#### Code Completion
+```python
+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
+```python
+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
+
+```python
+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:
+
+```bash
+<｜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:
+
+```bash
+<｜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](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
+
+```python
+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](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). 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]]([email protected]).