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DeepSeek Coder

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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.

2. Evaluation Results

DeepSeek Coder

3. How to Use

Here give some examples of how to use our model.

Chat Model Inference

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)

prompt= "plot a cgart for visualising employee and their years of experience.Assume any sample data df"

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 for more details.

5. Contact

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

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