---
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|