README.md

---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
pipeline_tag: text-generation
widget:
  - text: "How many helicopters can a human eat in one sitting?"
tags:
- Δ
- LoRA
---

<!--
# Model Card for Model ID
-->

## Model Details

$$
🗽_{ΔLoRA} = \Delta_{(🦙_W, 🗽_W)}
$$

$$
🦙_W + 🗽_{ΔLoRA} = 🗽_W \\
🦙_W - 🗽_{ΔLoRA} = 🦙_W
$$



<!--![image/png](https://cdn-uploads.huggingface.co/production/uploads/648b0f4fd8fe693f51de98d2/aerBANxBtCya732NdBiw0.png)-->
<!-- $$
W_{Llama3} + ΔLoRA_{Hermes} = W_{Hermes} \\
W_{Hermes} - ΔLoRA_{Hermes} = W_{Llama3}
$$
 -->
<!--
$$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} $$
```
typeof/zephyr-7b-beta-lora + mistralai/Mistral-7B-v0.1
= HuggingFaceH4/zephyr-7b-beta
````

### Model Description

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]


### Model Sources [optional]

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

## Uses

### Direct Use

[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

[More Information Needed]

## Bias, Risks, and Limitations

[More Information Needed]

### Recommendations

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

[NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)


## How to Get Started with the Model

<!-- [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) -->
<!-- ### Find the original here https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B -->


Use the code below to get started with the model.

```python
# pip install transformers peft

import torch
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

model_id = "meta-llama/Meta-Llama-3-8B"
peft_model_id = "typeof/Hermes-2-Pro-Llama-3-8B-delta-lora"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

system_prompt = """You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.
Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n    Args:\\n        symbol (str): The stock symbol.\\n\\n    Returns:\\n        dict: A dictionary containing fundamental data.\\n            Keys:\\n                - \'symbol\': The stock symbol.\\n                - \'company_name\': The long name of the company.\\n                - \'sector\': The sector to which the company belongs.\\n                - \'industry\': The industry to which the company belongs.\\n                - \'market_cap\': The market capitalization of the company.\\n                - \'pe_ratio\': The forward price-to-earnings ratio.\\n                - \'pb_ratio\': The price-to-book ratio.\\n                - \'dividend_yield\': The dividend yield.\\n                - \'eps\': The trailing earnings per share.\\n                - \'beta\': The beta value of the stock.\\n                - \'52_week_high\': The 52-week high price of the stock.\\n                - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}}  </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call>"""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Fetch the stock fundamentals data for Tesla (TSLA)"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
... call tool and pass back prompt like so...
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```

<!--

## Training Details

### Training Data


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### Training Procedure


#### Preprocessing [optional]

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#### Training Hyperparameters

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


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## Evaluation


### Testing Data, Factors & Metrics

#### Testing Data


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#### Factors


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### Results

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#### Summary

## Model Examination [optional]

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### Model Architecture and Objective

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## Training procedure

The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_4bit: True
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True

### Framework versions

- PEFT 0.6.3.dev0

-->
#### Summary

[LoRA](https://arxiv.org/abs/2305.14314)
[QLoRA](https://arxiv.org/abs/2106.09685)