README.md

---
language:
- en
license: mit
library_name: peft
datasets:
- AtlasUnified/atlas-storyteller
metrics:
- perplexity
base_model: mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
model-index:
- name: Mistral-7B-LoreWeaver
  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: 59.98
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      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: 83.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      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: 64.12
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      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: 42.15
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      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: 78.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      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: 37.68
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Reverb/Mistral-7B-LoreWeaver
      name: Open LLM Leaderboard
---

# Model Card for Model ID

Our finetuned Mistral LLM is a large language model specialized for natural language processing tasks, delivering enhanced performance for a 
wide array of applications, including text classification, question-answering, chatbot services, and more.



## Model Details

### Model Description

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



- **Developed by:** Basel Anaya, Osama Awad, Yazeed Mshayekh
- **Funded by [optional]:** Basel Anaya, Osama Awad, Yazeed Mshayekh
- **Model type:** Autoregressive Language Model
- **Language(s) (NLP):** English
- **License:** MIT License
- **Finetuned from model:** MistralAI's Mistral-7B

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model.
### Direct Use

Users can leverage the finetuned Mistral LLM for various NLP tasks right out-of-the-box. Simply interact with the API or load the model locally to experience superior language understanding and generation capabilities. Ideal for developers seeking rapid prototyping and deployment of conversational AI applications.

### Downstream Use [optional]

Integrate the finetuned Mistral LLM effortlessly into custom applications and pipelines. Utilize the model as a starting point for further refinement, targeting industry-specific lingo, niches, or particular use cases. Seamless compatibility ensures smooth collaboration with adjacent technologies and services.

### Out-of-Scope Use

Limitations exist concerning controversial topics, sensitive data, and scenarios demanding real-time responses. Users should exercise caution when deploying the model in safety-critical situations or regions with strict compliance regulations. Avoid sharing confidential or personally identifiable information with the model.

## Bias, Risks, and Limitations

Address both technical and sociotechnical limitations.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Further recommendations include cautious assessment of ethical implications, ongoing maintenance, periodic evaluations, and responsible reporting practices.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import pipeline, AutoTokenizer

# Load the finetuned Mistral LLM
model_name = "Reverb/Mistral-7B-LoreWeaver"
tokenizer = AutoTokenizer.from_pretrained(model_name)
generator = pipeline("text-generation", model=model_name, tokenizer=tokenizer)

# Example usage
input_text = "Once upon a time,"
num_generated_tokens = 50

response = generator(input_text, max_length=num_generated_tokens, num_return_sequences=1)
print(f"Generated text:\n{response[0]['generated_text']}")

# Alternatively, for fine-grained control over the generation process
inputs = tokenizer(input_text, return_tensors="pt")
outputs = generator.generate(
    inputs["input_ids"].to("cuda"),
    max_length=num_generated_tokens,
    num_beams=5,
    early_stopping=True,
    temperature=1.2,
)
generated_sentence = tokenizer.decode(outputs[0])
print(f"\nGenerated text with beam search and custom params:\n{generated_sentence}")
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## 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]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]


### Framework versions

- PEFT 0.7.1
# [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_Reverb__Mistral-7B-LoreWeaver)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |60.93|
|AI2 Reasoning Challenge (25-Shot)|59.98|
|HellaSwag (10-Shot)              |83.29|
|MMLU (5-Shot)                    |64.12|
|TruthfulQA (0-shot)              |42.15|
|Winogrande (5-shot)              |78.37|
|GSM8k (5-shot)                   |37.68|