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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## 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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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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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.

Import important libraries
```
import transformers
import torch
from transformers import pipeline
import accelerate
```

Prepare model and tokenizer
```
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "pankaj9075rawat/DevsDoCode_LLama-3-8b-Uncensored"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
```

Build Pipeline for text generation
```
pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    # model_kwargs={"torch_dtype": torch.bfloat16},
    # device="cuda",
    # device_map="auto",
    # token=access_token
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
```

Build response function
```
def get_response(
          query, message_history=[], max_tokens=128, temperature=1.1, top_p=0.9
      ):
    user_prompt = message_history + [{"role": "user", "content": query}]
    prompt = pipeline.tokenizer.apply_chat_template(
        user_prompt, tokenize=False, add_generation_prompt=True
    )
    # print("prompt before coversion: ", user_prompt)
    # print("prompt after conversion: ", prompt)
    outputs = pipeline(
        prompt,
        max_new_tokens=max_tokens,
        eos_token_id=terminators,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
    )
    response = outputs[0]["generated_text"][len(prompt):]
    return response, user_prompt + [{"role": "assistant", "content": response}]
```

Build chat on notebook itself (define a system prompt variable)
```
convers = [{"role": "system", "content": system_instruction}]


def chat():
    global convers 
    response, convers = get_init_AI_response(convers)
    print("response:", response)

    while True:
        user_input = input("enter chat")
        if user_input.lower() in ["exit", "quit"]:
            return {"response": "Exiting the chatbot. Goodbye!"}

        response, convers = get_response(user_input, convers)
        print("response:", response)

chat()
```


[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### 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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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### 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]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **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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