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OlgunLLM

Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
finance
economics
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Initial commit of OlgunLLM

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  1. .gitattributes +1 -0
  2. README.md +336 -0
  3. config.json +40 -0
  4. generation_config.json +11 -0
  5. model.safetensors.index.json +298 -0
  6. pytorch_model-00001-of-00004.safetensors +3 -0
  7. pytorch_model-00002-of-00004.safetensors +3 -0
  8. pytorch_model-00003-of-00004.safetensors +3 -0
  9. pytorch_model-00004-of-00004.safetensors +3 -0
  10. safe.py +44 -0
  11. special_tokens_map.json +23 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +2066 -0
.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,336 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - text-generation-inference
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+ - finance
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+ - economics
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+ datasets:
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+ - Josephgflowers/Finance-Instruct-500k
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+ language:
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+ - en
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+ base_model:
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+ - unsloth/Meta-Llama-3.1-8B
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ ---
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+
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+ # Model Card for Finance-Llama-8B
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+
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+ This model is a fine-tuned version of `unsloth/Meta-Llama-3.1-8B` on the `Josephgflowers/Finance-Instruct-500k` dataset. It's designed for financial tasks, reasoning, and multi-turn conversations.
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+
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+ ## Key Features
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+
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+ * **Extensive Coverage:** Trained on over 500,000 entries spanning financial QA, reasoning, sentiment analysis, topic classification, multilingual NER, and conversational AI.📚
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+ * **Multi-Turn Conversations:** Capable of rich dialogues emphasizing contextual understanding and reasoning.
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+ * **Diverse Data Sources:** Includes entries from Cinder, Sujet-Finance-Instruct-177k, Phinance Dataset, BAAI/IndustryInstruction_Finance-Economics, Josephgflowers/Financial-NER-NLP, and many other high-quality datasets.
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+ * **Financial Specialization:** Tailored for financial reasoning, question answering, entity recognition, sentiment analysis, and more.
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+
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+ ## Author & Opportunities
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+ Tarun Sai Goddu - Data Scientist at Jio Platforms Ltd (2+ years experience) | IIT Bombay
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+
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+ Expertise: AI Agents • RAG Pipelines • Computer Vision • NLP • Speech Domain
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+
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+ **Actively seeking opportunities** as an **ML Engineer II / Data Scientist II** where I can contribute to building scalable, production-ready AI/ML systems.
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+
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+ **Reach me here:**
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+ - LinkedIn: [linkedin.com/in/tarunsaigoddu](https://www.linkedin.com/in/tarunsaigoddu) | Email: [[email protected]](mailto:[email protected])
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+ - GitHub: [github.com/tarun7r](https://github.com/tarun7r)
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+
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+
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+ ## Dataset Details 💾
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+
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+ ### Finance-Instruct-500k Dataset
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+
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+ **Overview**
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+ Finance-Instruct-500k is a comprehensive and meticulously curated dataset designed to train advanced language models for financial tasks, reasoning, and multi-turn conversations. Combining data from numerous high-quality financial datasets, this corpus provides over 500,000 entries, offering unparalleled depth and versatility for finance-related instruction tuning and fine-tuning.
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+
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+ The dataset includes content tailored for financial reasoning, question answering, entity recognition, sentiment analysis, address parsing, and multilingual natural language processing (NLP). Its diverse and deduplicated entries make it suitable for a wide range of financial AI applications, including domain-specific assistants, conversational agents, and information extraction systems.
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+
49
+ **Key Features of the Dataset**
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+ * **Extensive Coverage:** Over 500,000 entries spanning financial QA, reasoning, sentiment analysis, topic classification, multilingual NER, and conversational AI.🌍
51
+ * **Multi-Turn Conversations:** Rich dialogues emphasizing contextual understanding and reasoning.🗣️
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+ * **Diverse Data Sources:** Includes entries from Cinder, Sujet-Finance-Instruct-177k, Phinance Dataset, BAAI/IndustryInstruction_Finance-Economics, Josephgflowers/Financial-NER-NLP, and many other high-quality datasets. 📖
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+
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+ ## CFA Level 1 Mock Exam Results
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+
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+ The CFA (Chartered Financial Analyst) exam is widely recognized as one of the most challenging professional certifications in the financial industry, typically requiring over 1000 hours of study across all three levels. The evaluation concept for the CFA Level 1 mock exam was inspired by the work on [mukaj/Llama-3.1-Hawkish-8B](https://huggingface.co/mukaj/Llama-3.1-Hawkish-8B). Below is a comparison of different models on a sample Level 1 CFA Mock Exam, demonstrating how Finance-Llama-8B performs on the exam. The same prompt was used for all models. The results presented are approximated and have been tested across multiple mock exam papers to ensure consistency. A sample mock exam with a comparison to other models is shown below.
57
+
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+ <table>
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+ <thead>
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+ <tr>
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+ <th>CFA Level 1</th>
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+ <th>GPT-4o-mini (%)</th>
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+ <th>Finance-Llama-8B (%)</th>
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+ <th>Meta-Llama Instruct 8B (%)</th>
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+ <th>Meta-Llama Instruct 70B (%)</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ <tr>
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+ <td>Ethical and Professional Standards</td>
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+ <td>80</td>
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+ <td>76</td>
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+ <td>56</td>
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+ <td>68</td>
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+ </tr>
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+ <tr>
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+ <td>Quantitative Methods</td>
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+ <td>74</td>
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+ <td>73</td>
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+ <td>64</td>
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+ <td>85</td>
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+ </tr>
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+ <tr>
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+ <td>Economics</td>
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+ <td>69</td>
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+ <td>74</td>
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+ <td>59</td>
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+ <td>59</td>
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+ </tr>
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+ <tr>
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+ <td>Financial Reporting</td>
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+ <td>81</td>
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+ <td>77</td>
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+ <td>67</td>
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+ <td>71</td>
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+ </tr>
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+ <tr>
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+ <td>Corporate Finance</td>
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+ <td>82</td>
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+ <td>71</td>
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+ <td>51</td>
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+ <td>80</td>
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+ </tr>
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+ <tr>
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+ <td>Equity Investments</td>
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+ <td>53</td>
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+ <td>67</td>
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+ <td>43</td>
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+ <td>66</td>
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+ </tr>
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+ <tr>
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+ <td>Fixed Income</td>
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+ <td>80</td>
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+ <td>72</td>
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+ <td>29</td>
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+ <td>51</td>
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+ </tr>
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+ <tr>
119
+ <td>Derivatives</td>
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+ <td>54</td>
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+ <td>72</td>
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+ <td>34</td>
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+ <td>35</td>
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+ </tr>
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+ <tr>
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+ <td>Alternative Investments</td>
127
+ <td>100</td>
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+ <td>89</td>
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+ <td>74</td>
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+ <td>100</td>
131
+ </tr>
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+ <tr>
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+ <td>Portfolio Management</td>
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+ <td>85</td>
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+ <td>75</td>
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+ <td>52</td>
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+ <td>100</td>
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+ </tr>
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+ <tr>
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+ <td><b>Weighted Average</b></td>
141
+ <td><b>75</td>
142
+ <td><b>73</td>
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+ <td>53</td>
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+ <td><b>70</td>
145
+ </tr>
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+ <tr>
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+ <td><b>Result</b></td>
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+ <td><b>PASS</b></td>
149
+ <td><b>PASS</b></td>
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+ <td><b>FAIL</b></td>
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+ <td><b>PASS</b></td>
152
+ </tr>
153
+ </tbody>
154
+ </table>
155
+
156
+ The mock exams are designed to challenge candidates with varying levels of difficulty, reflecting the rigorous nature of the CFA Level 1 exam. Pass rates for these mock exams typically range from 64% to 72%, with an average pass rate of around 67%. This average is notably higher than the 12-year average Minimum Passing Score (MPS) of 65% for all CFA years, indicating the effectiveness of the preparation materials. For more detailed insights, visit [300hours.com/cfa-passing-score](https://300hours.com/cfa-passing-score/).
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+
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+
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+ ## Usage
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+
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+ ### Ollama
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+
163
+ You can also use this model with Ollama. Pre-built GGUF versions (FP16 and Q4_K_M) are available at:
164
+ [ollama.com/martain7r/finance-llama-8b](https://ollama.com/martain7r/finance-llama-8b)
165
+
166
+ To run the FP16 version:
167
+ ```bash
168
+ ollama run martain7r/finance-llama-8b:fp16
169
+ ```
170
+
171
+ To run the Q4_K_M quantized version (smaller and faster, with a slight trade-off in quality):
172
+ ```bash
173
+ ollama run martain7r/finance-llama-8b:q4_k_m
174
+ ```
175
+
176
+ This model can be used with the `transformers` library pipeline for text generation.
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+
178
+ First, make sure you have the `transformers` and `torch` libraries installed:
179
+
180
+ ````bash
181
+ pip install transformers torch
182
+ ````
183
+
184
+ **Usage 🚀**
185
+ **Transformers Pipeline**
186
+
187
+ ````bash
188
+ from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
189
+ import torch
190
+
191
+ # Alternative memory-efficient loading options without bitsandbytes
192
+
193
+ model_id = "tarun7r/Finance-Llama-8B"
194
+
195
+ print("Loading model with memory optimizations...")
196
+
197
+ # Option 1: Use FP16 (half precision) - reduces memory by ~50%
198
+ try:
199
+ print("Trying FP16 loading...")
200
+ model = AutoModelForCausalLM.from_pretrained(
201
+ model_id,
202
+ torch_dtype=torch.float16, # Half precision
203
+ device_map="auto", # Automatic device placement
204
+ low_cpu_mem_usage=True, # Efficient CPU memory usage during loading
205
+ trust_remote_code=True
206
+ )
207
+ print("✓ Model loaded with FP16")
208
+
209
+ except Exception as e:
210
+ print(f"FP16 loading failed: {e}")
211
+
212
+ # Option 2: CPU offloading - some layers on GPU, some on CPU
213
+ try:
214
+ print("Trying CPU offloading...")
215
+ model = AutoModelForCausalLM.from_pretrained(
216
+ model_id,
217
+ torch_dtype=torch.float16,
218
+ device_map="balanced", # Balance between GPU and CPU
219
+ low_cpu_mem_usage=True,
220
+ trust_remote_code=True
221
+ )
222
+ print("✓ Model loaded with CPU offloading")
223
+
224
+ except Exception as e:
225
+ print(f"CPU offloading failed: {e}")
226
+
227
+ # Option 3: Full CPU loading as fallback
228
+ print("Loading on CPU...")
229
+ model = AutoModelForCausalLM.from_pretrained(
230
+ model_id,
231
+ torch_dtype=torch.float16,
232
+ device_map="cpu",
233
+ low_cpu_mem_usage=True,
234
+ trust_remote_code=True
235
+ )
236
+ print("✓ Model loaded on CPU")
237
+
238
+ # Load tokenizer
239
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
240
+ if tokenizer.pad_token is None:
241
+ tokenizer.pad_token = tokenizer.eos_token
242
+
243
+ # Create pipeline
244
+ generator = pipeline(
245
+ "text-generation",
246
+ model=model,
247
+ tokenizer=tokenizer
248
+ )
249
+
250
+ print("✓ Pipeline created successfully!")
251
+
252
+
253
+ # Your existing prompt code
254
+ finance_prompt_template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
255
+
256
+ ### Instruction:
257
+ {}
258
+
259
+ ### Input:
260
+ {}
261
+
262
+ ### Response:
263
+ """
264
+
265
+ # Update the system prompt to provide a more detailed description of the chatbot's role
266
+ messages = [
267
+ {"role": "system", "content": "You are a highly knowledgeable finance chatbot. Your purpose is to provide accurate, insightful, and actionable financial advice to users, tailored to their specific needs and contexts."},
268
+ {"role": "user", "content": "What strategies can an individual investor use to diversify their portfolio effectively in a volatile market?"},
269
+ ]
270
+
271
+ # Update the generator call to use the messages
272
+ prompt = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in messages])
273
+
274
+ print("\n--- Generating Response ---")
275
+
276
+ try:
277
+ outputs = generator(
278
+ prompt,
279
+ #max_new_tokens=250, # Reduced for memory efficiency
280
+ do_sample=True,
281
+ temperature=0.7,
282
+ top_p=0.9,
283
+ pad_token_id=tokenizer.eos_token_id,
284
+ # Memory efficient generation settings
285
+ num_beams=1, # No beam search to save memory
286
+ early_stopping=True,
287
+ use_cache=True
288
+ )
289
+
290
+ # Extract response
291
+ generated_text = outputs[0]['generated_text']
292
+ response_start = generated_text.rfind("### Response:")
293
+ if response_start != -1:
294
+ response = generated_text[response_start + len("### Response:"):].strip()
295
+ print("\n--- Response ---")
296
+ print(response)
297
+ else:
298
+ print(generated_text)
299
+
300
+ # Clean up GPU memory after generation
301
+ if torch.cuda.is_available():
302
+ torch.cuda.empty_cache()
303
+
304
+ except Exception as e:
305
+ print(f"Generation error: {e}")
306
+
307
+
308
+ ````
309
+ **Citation 📌**
310
+ ````
311
+ @misc{tarun7r/Finance-Llama-8B,
312
+ author = {tarun7r},
313
+ title = {tarun7r/Finance-Llama-8B: A Llama 3.1 8B Model Fine-tuned on Josephgflowers/Finance-Instruct-500k},
314
+ year = {2025},
315
+ publisher = {Hugging Face},
316
+ journal = {Hugging Face Model Hub},
317
+ howpublished = {\url{https://huggingface.co/tarun7r/Finance-Llama-8B}}
318
+ }
319
+
320
+ ````
321
+
322
+ ## Disclaimer & Intended Uses
323
+
324
+ ### Model & License
325
+ This model is an experimental research implementation based on Meta's LLaMA 3.1 architecture and is governed by the LLaMA 3.1 community license terms, with additional restrictions as outlined below. It is designed for academic and research purposes to explore the influence of financial data in training language models. Users are advised that this model is experimental and should be used at their own risk, with full responsibility for any implementation or application. **This model is not a financial advisor and should not be used for financial decision-making.**
326
+
327
+ ### Liability & Responsibility
328
+ The creators of this model:
329
+ - Accept no responsibility for any use of the model, including any financial losses or damages incurred.
330
+ - Provide no warranties or guarantees regarding its performance, accuracy, or reliability.
331
+ - Make no claims about the suitability of the model for any specific purpose.
332
+
333
+ ### Intellectual Property & Attribution
334
+ - All findings and opinions expressed are solely those of the authors.
335
+ - This model is not endorsed by or affiliated with Meta, the CFA Institute, or any other institutions.
336
+ - All trademarks and intellectual property rights belong to their respective owners.
config.json ADDED
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1
+ {
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+ "architectures": [
3
+ "LlamaForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 128000,
8
+ "eos_token_id": 128001,
9
+ "head_dim": 128,
10
+ "hidden_act": "silu",
11
+ "hidden_size": 4096,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 14336,
14
+ "max_position_embeddings": 131072,
15
+ "mlp_bias": false,
16
+ "model_type": "llama",
17
+ "num_attention_heads": 32,
18
+ "num_hidden_layers": 32,
19
+ "num_key_value_heads": 8,
20
+ "pad_token_id": 128004,
21
+ "pretraining_tp": 1,
22
+ "rms_norm_eps": 1e-05,
23
+ "rope_scaling": {
24
+ "factor": 8.0,
25
+ "high_freq_factor": 4.0,
26
+ "low_freq_factor": 1.0,
27
+ "original_max_position_embeddings": 8192,
28
+ "rope_type": "llama3"
29
+ },
30
+ "rope_theta": 500000.0,
31
+ "tie_word_embeddings": false,
32
+ "torch_dtype": "float16",
33
+ "transformers_version": "4.51.3",
34
+ "unsloth_fixed": true,
35
+ "unsloth_version": "2025.5.7",
36
+ "use_cache": true,
37
+ "vocab_size": 128256,
38
+ "model_name": "OlgunLLM",
39
+ "description": "My renamed and fine-tuned finance model"
40
+ }
generation_config.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 128000,
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+ "do_sample": true,
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+ "eos_token_id": 128001,
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+ "max_length": 131072,
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+ "pad_token_id": 128004,
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+ "temperature": 0.6,
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+ "top_p": 0.9,
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+ "transformers_version": "4.51.3"
11
+ }
model.safetensors.index.json ADDED
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+ {
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+ "metadata": {
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pytorch_model-00001-of-00004.safetensors ADDED
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+ size 4976698592
pytorch_model-00002-of-00004.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:45f4aeb19a1b9bc93161a0c887e5793c7528d29d5744fd6e7a3bdbdf8a6b1692
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+ size 4999802616
pytorch_model-00003-of-00004.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:89cb5929bf924cd3936409a97a0e1071eace8b4976a7355cfe821068eea8d966
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+ size 4915916080
pytorch_model-00004-of-00004.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b665ba94a043aaa20672dd38661974ae23af59db44e4c306fd0e0fce8d44557c
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+ size 1168138808
safe.py ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
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+ from safetensors import safe_open
3
+
4
+ def generate_safetensors_index(model_path="."):
5
+ """Generate model.safetensors.index.json from existing safetensors files"""
6
+
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+ # Load the existing bin index as reference
8
+ with open(f"pytorch_model.bin.index.json", "r") as f:
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+ bin_index = json.load(f)
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+
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+ # Initialize the safetensors index structure
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+ safetensors_index = {
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+ "metadata": bin_index.get("metadata", {}),
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+ "weight_map": {}
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+ }
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+
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+ # Map each safetensors file and get its tensor names
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+ safetensors_files = [
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+ "pytorch_model-00001-of-00004.safetensors",
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+ "pytorch_model-00002-of-00004.safetensors",
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+ "pytorch_model-00003-of-00004.safetensors",
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+ "pytorch_model-00004-of-00004.safetensors"
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+ ]
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+
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+ for safetensor_file in safetensors_files:
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+ try:
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+ with safe_open(f"{safetensor_file}", framework="pt") as f:
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+ for tensor_name in f.keys():
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+ safetensors_index["weight_map"][tensor_name] = safetensor_file
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+ print(f"✓ Processed {safetensor_file}")
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+ except Exception as e:
32
+ print(f"✗ Error processing {safetensor_file}: {e}")
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+
34
+ # Save the index file
35
+ with open(f"model.safetensors.index.json", "w") as f:
36
+ json.dump(safetensors_index, f, indent=2)
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+
38
+ print(f"✓ Generated model.safetensors.index.json with {len(safetensors_index['weight_map'])} tensors")
39
+ return safetensors_index
40
+
41
+ # Run the function
42
+ if __name__ == "__main__":
43
+ # Change this path to your model directory if needed
44
+ generate_safetensors_index("./Finance-Llama-8B")
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+ }
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+ }
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+ version https://git-lfs.github.com/spec/v1
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+ size 17209920
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1987
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1995
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2000
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2011
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2013
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2019
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2045
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2046
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2047
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2048
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2049
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2058
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2059
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2060
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2061
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2062
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2063
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2064
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2065
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2066
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