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---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5
Deepseek-Coder-7B-Instruct-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, and then fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 2. Evaluation Results
<img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png">
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
import ctranslate2
import transformers
from huggingface_hub import snapshot_download
model_id = "ByteForge/DS-7b-1.5_Instruct-ct2-int8_float32"
model_path = snapshot_download(model_id)
model = ctranslate2.Generator(model_path, device='cuda')
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
messages = [
{"role": "system", "content": "You are world class python programmer with deep expertise in Ploty for data visualisation and analysis. Given a input question and schema, answer with correct python plotly code"},
{"role": "user", "content": prompt},
]
input_ids = tokenizer1.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
tokenizer1.eos_token_id,
tokenizer1.convert_tokens_to_ids("<|eot_id|>")
]
input_tokens = tokenizer1.convert_ids_to_tokens(tokenizer1.encode(input_ids))
results = model1.generate_batch([input_tokens], include_prompt_in_result=False, max_length=700, sampling_temperature=0.6, sampling_topp=0.9, end_token=terminators)
output = tokenizer1.decode(results[0].sequences_ids[0])
print(output)
```
### 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](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
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