README.md

---
library_name: transformers
tags:
- LLM
pipeline_tag: text-generation
---

# Model Card for Model ID

This is a fine-tuned version of zephyr-7b-beta model on math dataset where the problems are generally based on probability, permutations and combinations.



## Model Details

### Model Description

This model is currently under developement but you can try it on your system

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.
```python
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)


model_id = "zephyr-7b-beta-Agent-Instruct-math"
base_model = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
tokenizer.padding_side = 'right'
tokenizer.pad_token = tokenizer.eos_token
tokenizer.add_eos_token = True
tokenizer.add_bos_token, tokenizer.add_eos_token


pipe = pipeline(task="text-generation", model=model_id, tokenizer=tokenizer, max_length=600)
prompt = "Enter your query"
result = pipe(prompt)
print(result[0]['generated_text'])

```

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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