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---
datasets:
- phatvo/hotpotqa-raft-dev-100
library_name: transformers
license: llama3.1
metrics:
- f1
- exact_match
pipeline_tag: text-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
LORA adapters of `meta-llama/Meta-Llama-3.1-8B-Instruct`, trained on 100 context samples from the HotpotQA dataset using the RAFT method, enable the model to better reason through the context and return more accurate outcomes.
### Evaluation
Evaluated on FULL validation set of HotpotQA.
| type | exatch_match| f1 | precision | recall |
|--------------|-------------|------------|---------------|---------|
| pretrained | 0.2980 | 0.3979 | 0.4116 | 0.5263 |
| finetuned | 0.3606 | **0.4857** | 0.4989 | 0.5318 |
Finetuned version increases **22% on F1 and 15% on average**
### Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_id = "phatvo/Meta-Llama3.1-8B-Instruct-RAFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", revision="main", trust_remote_code=True)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
inst = "Given the question and context below, thinking in logical reasoning way for your answer.\
Please provide only your answer in this format: CoT Answer: {reason} <ANSWER>: {answer}."
context = ""
question = ""
prompt = f"{context}\n{question}"
chat = [
{"role": "system", "content": inst},
{"role": "user", "content": prompt},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False)
output = pipe(prompt,
temperature=0.001,
max_new_tokens=1024, # recommended to set it more than 800
return_full_text=False,
do_sample=True)
print(output[0]["generated_text"])
# CoT Answer: thoughts... <ANSWER>: final_answer...
``` |