--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - orpo base_model: cognitivecomputations/dolphin-2.9-llama3-8b --- # This content is strictly for educational purposes and should not be construed as financial advice. Please exercise caution when applying any information provided. # Uploaded model - **Developed by:** baconnier - **License:** apache-2.0 - **Finetuned from model :** cognitivecomputations/dolphin-2.9-llama3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) This model was trained ORPO , using ChatML prompt template format. ``` <|im_start|>user Act as an exotic structurator and do not hesitate to merge standard and exotic products. Currently, inflation is 5% and 1-year swaps are valued at 6%. I expect inflation to reach 15% by the end of the year. Can you create the 10 most complicated structured derivative products to handle this scenario? Rank them by profitability, give me a score for profitability and another for risk from 0 to 10. Add an explaination for each structured products in maximum of 3 sentences. Think step by step and give me a concise, bulleted answer.<|im_end|> <|im_start|>assistant ``` # Example with local TGI: See the snippet below for usage with local inference: ```python #Example: reuse your existing OpenAI setup from openai import OpenAI client = OpenAI(base_url="http://localhost:8080/v1", api_key="TGI") completion = client.chat.completions.create( model="baconnier/finance_dolphin_orpo_llama3_8B_r64_51K_GGUF-unsloth.Q4_K_M", messages=[ {"role": "system", "content": "Act as a senior banker answering in 3 sentences in bullet points format."}, {"role": "user", "content": " What is CDS compare it to a swap "}, {"role": "assistant", "content": ""} ], max_tokens=400, temperature=0.7, ) print(completion.choices[0].message.content) ``` Output: ``` CDS: - Credit Default Swap (CDS) is a financial derivative contract between two parties (buyer and seller) for insurance against default or credit risk associated with a bond or loan - Protection buyer pays a premium to protection seller in exchange for the right to receive payment if a credit event occurs - Protection seller is usually a financial institution, while protection buyer can be an investor or a bank Swap: - Swap is an agreement between two parties to exchange cash flows, typically interest payments or principal at a future date - Common types of swaps include interest rate swaps, currency swaps, and commodity swaps CDS differs from swaps as it focuses on credit risk protection, while swaps involve cash flow exchange. ``` # Example with Transformers and pipeline: See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "baconnier/finance_dolphin_orpo_llama3_8B_r64_51K" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "user", "content": '''Act as an exotic structurator and do not hesitate to merge standard and exotic products. Currently, inflation is 5% and 1-year swaps are valued at 6%. I expect inflation to reach 15% by the end of the year. Can you create the 10 most complicated structured derivative products to handle this scenario? Rank them by profitability, give me a score for profitability and another for risk from 0 to 10. Add an explaination for each structured products in maximum of 3 sentences. Think step by step and give me a concise, bulleted answer.'''}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) outputs = pipeline( prompt, max_new_tokens=256, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` Answer: ``` To handle the scenario of inflation reaching 15% by the end of the year, I can create the following 10 most complicated structured derivative products, ranked by profitability and risk: 1. Inflation-linked swap spread strategy (Profitability: 9, Risk: 7) - Buy 1-year swaps and sell 15-year swaps to profit from the expected inflation increase. - Risk is moderate due to interest rate and inflation rate changes. 2. Inflation-linked bond spread strategy (Profitability: 8, Risk: 6) - Buy inflation-linked bonds and sell conventional bonds to profit from the inflation increase. - Risk is lower than the swap spread strategy due to the fixed maturity of bonds. 3. Inflation-linked option strategy (Profitability: 7, Risk: 5) - Buy call options on inflation-linked bonds to profit from the expected inflation increase. - Risk is lower than the swap spread strategy due to the limited downside. 4. Inflation-linked credit spread strategy (Profitability: 6, Risk: 4) - Buy high-yield inflation-linked bonds and sell investment-grade inflation-linked bonds to profit from the expected inflation increase. - Risk is moderate due to credit risk and interest rate changes. 5. Inflation-linked swap straddle strategy (Profitability: 5, Risk: 3) - Buy and sell 1-year and 15-year inflation swaps simultaneously to profit from the expected inflation increase. - Risk is moderate due to interest rate and inflation rate changes. 6. Inflation-linked bond straddle strategy (Profitability: 4, Risk: 2) - Buy and sell inflation-linked bonds with different maturities to profit from the expected inflation increase. - Risk is lower than the swap straddle strategy due to the fixed maturity of bonds. 7. Inflation-linked option straddle strategy (Profitability: 3, Risk: 1) - Buy and sell call options on inflation-linked bonds with different maturities to profit from the expected inflation increase. - Risk is lower than the swap straddle strategy due to the limited downside. 8. Inflation-linked credit spread straddle strategy (Profitability: 2, Risk: 0) - Buy and sell high-yield and investment-grade inflation-linked bonds with different maturities to profit from the expected inflation increase. - Risk is low due to the fixed maturity of bonds and the limited downside. 9. Inflation-linked swap spread strangle strategy (Profitability: 1, Risk: -1) - Buy and sell 1-year and 15-year inflation swaps with different strike prices to profit from the expected inflation increase. - Risk is high due to interest rate and inflation rate changes. 10. Inflation-linked bond spread strangle strategy (Profitability: 0, Risk: -2) - Buy and sell inflation-linked bonds with different strike prices to profit from the expected inflation increase. - Risk is very high due to the limited downside and the potential for significant losses. The most profitable strategies are the inflation-linked swap spread strategy and the inflation-linked bond spread strategy, with a profitability score of 9 and 8, respectively. The least profitable strategy is the inflation-linked bond spread strangle strategy, with a profitability score of 0. The most risky strategies are the inflation-linked swap spread strangle strategy and the inflation-linked bond spread strangle strategy, with a risk score of -1 and -2, respectively. The least risky strategy is the inflation-linked credit spread straddle strategy, with a risk score of 0. The 10 most complicated structured derivative products to handle the scenario of inflation reaching 15% by the end of the year are ranked by profitability and risk. The most profitable strategies are the inflation-linked swap spread strategy and the inflation-linked bond spread strategy, with a profitability score of 9 and 8, respectively. The least profitable strategy is the inflation-linked bond spread strangle strategy, with a profitability score of 0. The most risky strategies are the inflation-linked swap spread strangle strategy and the inflation-linked bond spread strangle strategy, with a risk score of -1 and -2, respectively. The least risky strategy is the inflation-linked credit spread straddle strategy, with a risk score of 0. ``` # Example with Transformers: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("baconnier/finance_dolphin_orpo_llama3_8B_r64_51K") model = AutoModelForCausalLM.from_pretrained("baconnier/finance_dolphin_orpo_llama3_8B_r64_51K") prompt = "What is CDS compare it to a swap" inputs = tokenizer(prompt, return_tensors="pt") # Generate generate_ids = model.generate(inputs.input_ids, max_length=200) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ```