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--- |
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library_name: peft |
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base_model: meta-llama/Meta-Llama-3-8B |
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language: |
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- en |
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pipeline_tag: text-generation |
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widget: |
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- text: "How many helicopters can a human eat in one sitting?" |
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tags: |
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- Δ |
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- LoRA |
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--- |
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<!-- |
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# Model Card for Model ID |
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--> |
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## Model Details |
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$$ |
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🗽_{ΔLoRA} = \Delta_{(🦙_W, 🗽_W)} |
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$$ |
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$$ |
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🦙_W + 🗽_{ΔLoRA} = 🗽_W \\ |
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🦙_W - 🗽_{ΔLoRA} = 🦙_W |
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$$ |
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<!----> |
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<!-- $$ |
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W_{Llama3} + ΔLoRA_{Hermes} = W_{Hermes} \\ |
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W_{Hermes} - ΔLoRA_{Hermes} = W_{Llama3} |
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$$ |
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--> |
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<!-- |
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$$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} $$ |
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``` |
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typeof/zephyr-7b-beta-lora + mistralai/Mistral-7B-v0.1 |
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= HuggingFaceH4/zephyr-7b-beta |
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```` |
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### Model Description |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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### Direct Use |
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[More Information Needed] |
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### Downstream Use [optional] |
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[More Information Needed] |
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### Out-of-Scope Use |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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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. |
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--> |
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### Model Sources |
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[NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) |
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## How to Get Started with the Model |
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<!-- [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) --> |
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<!-- ### Find the original here https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B --> |
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Use the code below to get started with the model. |
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```python |
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# pip install transformers peft |
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import torch |
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer |
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model_id = "meta-llama/Meta-Llama-3-8B" |
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peft_model_id = "typeof/Hermes-2-Pro-Llama-3-8B-delta-lora" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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model.load_adapter(peft_model_id) |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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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. |
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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: |
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<tool_call> |
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{"arguments": <args-dict>, "name": <function-name>} |
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</tool_call>""" |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": "Fetch the stock fundamentals data for Tesla (TSLA)"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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``` |
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<|im_start|>assistant |
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<tool_call> |
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{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"} |
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</tool_call><|im_end|> |
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``` |
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... call tool and pass back prompt like so... |
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``` |
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<|im_start|>tool |
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<tool_response> |
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{"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}} |
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</tool_response> |
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<|im_end|> |
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``` |
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``` |
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<|im_start|>assistant |
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The stock fundamentals data for Tesla (TSLA) are as follows: |
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- **Symbol**: TSLA |
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- **Company Name**: Tesla, Inc. |
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- **Sector**: Consumer Cyclical |
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- **Industry**: Auto Manufacturers |
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- **Market Capitalization**: $566,160,130,480 |
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- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73 |
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- **Price-to-Book Ratio (PB Ratio)**: 9.04 |
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- **Dividend Yield**: N/A |
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- **Trailing Earnings Per Share (EPS)**: $4.3 |
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- **Beta Value of the Stock**: 2.42 |
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- **52-Week High Price of the Stock**: $299.29 |
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- **52-Week Low Price of the Stock**: $152.37 |
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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|> |
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``` |
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<!-- |
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## Training Details |
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### Training Data |
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[More Information Needed] |
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### Training Procedure |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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#### Speeds, Sizes, Times [optional] |
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[More Information Needed] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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[More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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[More Information Needed] |
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## More Information |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_4bit: True |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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### Framework versions |
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- PEFT 0.6.3.dev0 |
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--> |
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#### Summary |
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[LoRA](https://arxiv.org/abs/2305.14314) |
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[QLoRA](https://arxiv.org/abs/2106.09685) |
