---
language:
- en
license: apache-2.0
model-index:
- name: SAM
  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: 59.39
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      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: 82.31
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      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: 62.15
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      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: 52.64
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      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: 76.4
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      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: 22.9
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=SuperAGI/SAM
      name: Open LLM Leaderboard
---
# Model Card
SAM (Small Agentic Model), a 7B model that demonstrates impressive reasoning abilities despite its smaller size. SAM-7B has outperformed existing SoTA models on various reasoning benchmarks, including GSM8k and ARC-C.

For full details of this model please read our [release blog post](https://superagi.com/introducing-sam-small-agentic-model/).

# Key Contributions
- SAM-7B outperforms GPT 3.5, Orca, and several other 70B models on multiple reasoning benchmarks, including ARC-C and GSM8k.
- Interestingly, despite being trained on a 97% smaller dataset, SAM-7B surpasses Orca-13B on GSM8k.
- All responses in our fine-tuning dataset are generated by open-source models without any assistance from state-of-the-art models like GPT-3.5 or GPT-4.

## Training
  - Trained by: SuperAGI Team
  - Hardware: NVIDIA 6 x H100 SxM (80GB)
  - Model used: Mistral 7B
  - Duration of finetuning: 4 hours
  - Number of epochs: 1
  - Batch size: 16
  - Learning Rate: 2e-5
  - Warmup Ratio: 0.1
  - Optmizer: AdamW
  - Scheduler: Cosine

## Example Prompt

The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.


## Evaluation

These benchmarks show that our model has improved reasoning as compared to orca 2-7b, orca 2-13b and GPT-3.5.
Despite being smaller in size, we show better multi-hop reasoning, as shown below:
<img src = "https://superagi.com/wp-content/uploads/2023/12/image-932.png" alt="Reasoning Benchmark Performance" width="700"> 

Note: Temperature=0.3 is the suggested for optimal performance

## Run the model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SuperAGI/SAM"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Can elephants fly?"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```


## Limitations

SAM is a demonstration that better reasoning can be induced using less but high-quality data generated using OpenSource LLMs.
The model is not suitable for conversations and simple Q&A, it performs better in task breakdown and reasoning only.
It does not have any moderation mechanisms. Therefore, the model is not suitable for production usage as it doesn't have guardrails for toxicity, societal bias, and language limitations. We would love to collaborate with the community to build safer and better models.

## The SuperAGI AI Team
Anmol Gautam, Arkajit Datta, Rajat Chawla, Ayush Vatsal, Sukrit Chatterjee, Adarsh Jha, Abhijeet Sinha, Rakesh Krishna, Adarsh Deep, Ishaan Bhola, Mukunda NS, Nishant Gaurav.
# [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_SuperAGI__SAM)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |59.30|
|AI2 Reasoning Challenge (25-Shot)|59.39|
|HellaSwag (10-Shot)              |82.31|
|MMLU (5-Shot)                    |62.15|
|TruthfulQA (0-shot)              |52.64|
|Winogrande (5-shot)              |76.40|
|GSM8k (5-shot)                   |22.90|