README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Demo [optional]:** [More Information Needed]

## Driectly Uses

```

from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
from peft import PeftModelForCausalLM
from transformers import BitsAndBytesConfig
base_model = "ljcnju/DeepSeek7bForCodeTrans"
tokenzier = AutoTokenizer.from_pretrained(base_model)
babcfig = BitsAndBytesConfig(load_in_8bit=True,llm_int8_enable_fp32_cpu_offload=True)
basemodel = "deepseek-ai/deepseek-coder-6.7b-base"
model = AutoModelForCausalLM.from_pretrained(basemodel,
        device_map = "cuda:0",
        quantization_config = babcfig)
model.resize_token_embeddings(len(tokenzier))

model = PeftModelForCausalLM.from_pretrained(model,base_model)

prompt = "<|translate|> public void removePresentationFormat() {remove1stProperty(PropertyIDMap.PID_PRESFORMAT);}\n<|end_of_c-sharp_code|><|begin_of_c-sharp_code|>"
input = tokenzier(prompt,return_tensors="pt")
output_ids = model.generate(**input)
print(tokenzier.batch_decode(output_ids))

```

### Use with vLLM

```

from vllm import LLM, SamplingParams,EngineArgs, LLMEngine, RequestOutput
from vllm.lora.request import LoRARequest
engine_args = EngineArgs(model="deepseek-ai/deepseek-coder-6.7b-base",
                             enable_lora=True,
                             max_loras=1,
                             max_lora_rank=8,
                             max_cpu_loras=2,
                             max_num_seqs=256,
                             max_model_len= 512)
engine = LLMEngine.from_engine_args(engine_args)
lorarequest = LoRARequest("DeepSeek7bForCodeTrans",1,"ljcnju/DeepSeek7bForCodeTrans")
engine.add_lora(lorarequest)

additional_special_tokens = {'additional_special_tokens':['<|begin_of_java_code|>','<|end_of_java_code|>'\
                                                           ,'<|begin_of_c-sharp_code|>','<|end_of_c-sharp_code|>',\
                                                            '<|translate|>']}
prompt = "public void serialize(LittleEndianOutput out) {out.writeShort(field_1_vcenter);}\n"
prompt = additional_special_tokens['additional_special_tokens'][0] + prompt + additional_special_tokens['additional_special_tokens'][1] + additional_special_tokens['additional_special_tokens'][2]

sampling_params = SamplingParams(temperature=0.1,max_tokens= 512,stop_token_ids=[32022,32014],skip_special_tokens=False)
engine.add_request(str(1),prompt,sampling_params,lora_request=lorarequest)
engine.step()
real_output = ""
finished = False
while engine.has_unfinished_requests():
    request_outputs  =  engine.step()
    for request_output in request_outputs:
        finished = finished | request_output.finished
print(request_outputs[0].outputs[0].text)

```

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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