transformers safetensors arxiv:1910.09700 endpoints_compatible region:us
---
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.

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## Driectly Uses

```Python

from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
from peft import PeftModelForCausalLM
from transformers import BitsAndBytesConfig
adapter_model = "ljcnju/CodeLlama7bForCodeTransLoRA"
tokenzier = AutoTokenizer.from_pretrained(adapter_model)
babcfig = BitsAndBytesConfig(load_in_8bit=True,llm_int8_enable_fp32_cpu_offload=True)
basemodel = "codellama/CodeLlama-7b-hf"
model = AutoModelForCausalLM.from_pretrained(basemodel,
        device_map = "cuda:0",
        quantization_config = babcfig)
model.resize_token_embeddings(len(tokenzier))
model = PeftModelForCausalLM.from_pretrained(model,adapter_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

```Python

from vllm import LLM, SamplingParams,EngineArgs, LLMEngine, RequestOutput
from vllm.lora.request import LoRARequest
engine_args = EngineArgs(model="codellama/CodeLlama-7b-hf",
                             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("CodeLLaMAForCodeTrans",1,"ljcnju/CodeLlama7bForCodeTransLoRA")
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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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

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## Training Details

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#### Summary



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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).

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