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---
language:
- en
license: apache-2.0
datasets:
- Intel/orca_dpo_pairs
model-index:
- name: TinyLlama-1.1B-orca-v1.0
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 36.35
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 61.23
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 36.58
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 2.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sreeramajay/TinyLlama-1.1B-orca-v1.0
name: Open LLM Leaderboard
---
Applied DPO to TinyLlama-1.1B-Chat-v1.0 using orca_dpo_pairs dataset
This is only experimental Model created by following instruction from the nice Blog [Fine-tune a Mistral-7b model with Direct Preference Optimization
](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac)
You can run this model using the following code:
```python
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
# <|system|>
# You are a helpful assistant chatbot.</s>
# <|user|>
# What is a Large Language Model?</s>
# <|assistant|>
# A Large Language Model (LLM) is a type of deep learning model that processes large amounts of text or data to improve the accuracy of natural language processing tasks such as sentiment analysis, machine translation, and question answering. LLMs are trained using large datasets, which allow them to generalize better and have better performance compared to traditional machine learning models. They are capable of handling vast amounts of text and can learn complex relationships between words, phrases, and sentences, making them an essential tool for natural language processing.
```
Results on GPT4ALL benchmark:
| Tasks | Metric |Value | |Stderr|
|-------------|--------|-----:|---|-----:|
|arc_challenge|acc |0.3003|± |0.0134|
| |acc_norm|0.3276|± |0.0137|
|arc_easy |acc |0.6115|± |0.0100|
| |acc_norm|0.5354|± |0.0102|
|boolq |acc |0.6147|± |0.0085|
|hellaswag |acc |0.4633|± |0.0050|
| |acc_norm|0.6033|± |0.0049|
|openbookqa |acc |0.2480|± |0.0193|
| |acc_norm|0.3720|± |0.0216|
|piqa |acc |0.7470|± |0.0101|
| |acc_norm|0.7470|± |0.0101|
|winogrande |acc |0.6054|± |0.0137|
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sreeramajay__TinyLlama-1.1B-orca-v1.0)
| Metric |Value|
|---------------------------------|----:|
|Avg. |37.17|
|AI2 Reasoning Challenge (25-Shot)|36.35|
|HellaSwag (10-Shot) |61.23|
|MMLU (5-Shot) |25.18|
|TruthfulQA (0-shot) |36.58|
|Winogrande (5-shot) |61.40|
|GSM8k (5-shot) | 2.27|
|