---
library_name: transformers
tags:
- pytorch
- llama3
license: llama3
datasets:
- ruslanmv/ai-medical-chatbot
language:
- en
metrics:
- accuracy
---

# Model Card for Model ID

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



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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:** ManhTien(Money)
- **Funded by:** [More Information Needed]
- **Shared by :** [More Information Needed]
- **Model type:** natural language model - llm3
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct

### Usage

Here is an example of how to use the `tien007/llama-3-8b-sft` model with Hugging Face's Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_id = "tien007/llama-3-8b-sft"
model = AutoModelForCausalLM.from_pretrained(model_id, ignore_mismatched_sizes=True, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Define input messages
messages = [{ "role": "user", "content": "How to effectively treat cancer?" }]

# Prepare the prompt
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize input and move to GPU
inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True).to("cuda")

# Generate a response
outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)

# Decode the response
text = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Print the response
print(text.split("assistant")[1])
```

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

### Results


#### Summary


## 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]