Create README.md

README.md

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+---
+library_name: peft
+base_model: mistralai/Mistral-7B-v0.1
+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
+
+$$
+🦙_W + 🗽_{ΔLoRA} = 🗽_W \\
+🦙_W - 🗽_{ΔLoRA} = 🦙_W
+$$
+
+
+<!--![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]
+
+[More Information Needed]
+
+### 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.
+-->
+
+## How to Get Started with the Model
+
+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
+
+
+[More Information Needed]
+
+### Training Procedure
+
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+#### Speeds, Sizes, Times [optional]
+
+
+[More Information Needed]
+
+## Evaluation
+
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+
+[More Information Needed]
+
+#### Factors
+
+
+[More Information Needed]
+
+#### Metrics
+
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+## Model Examination [optional]
+
+[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]
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+[More Information Needed]
+
+## More Information
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+## 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)