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--- |
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license: apache-2.0 |
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datasets: |
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- uonlp/CulturaX |
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language: |
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- tr |
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- en |
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pipeline_tag: text-generation |
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metrics: |
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- accuracy |
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- bleu |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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--- |
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# Commencis-LLM |
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<!-- Provide a quick summary of what the model is/does. --> |
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Commencis LLM is a generative model based on the Mistral 7B model. The base model adapts Mistral 7B to Turkish Banking specifically by training on a diverse dataset obtained through various methods, encompassing general Turkish and banking data. |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Commencis](https://www.commencis.com) |
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- **Language(s):** Turkish |
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- **Finetuned from model:** [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) |
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- **Blog Post**: [LLM Blog](https://www.commencis.com/thoughts/commencis-introduces-its-purpose-built-turkish-fluent-llm-for-banking-and-finance-industry-a-detailed-overview/) |
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## Training Details |
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Alignment phase consists of two stages: supervised fine-tuning (SFT) and Reward Modeling with Reinforcement learning from human feedback (RLHF). |
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The SFT phase was done on the a mixture of synthetic datasets generated from comprehensive banking dictionary data, synthetic datasets generated from banking-based domain and sub-domain headings, and derived from the CulturaX Turkish dataset by filtering. It was trained with three epochs. We used a learning rate 2e-5, lora rank 64 and maximum sequence length 1024 tokens. |
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### Usage |
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### Suggested Inference Parameters |
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- Temperature: 0.5 |
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- Repetition penalty: 1.0 |
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- Top-p: 0.9 |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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class TextGenerationAssistant: |
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def __init__(self, model_id:str): |
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self.tokenizer = AutoTokenizer.from_pretrained(model_id) |
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self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto',load_in_8bit=True,load_in_4bit=False) |
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self.pipe = pipeline("text-generation", |
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model=self.model, |
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tokenizer=self.tokenizer, |
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device_map="auto", |
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max_new_tokens=1024, |
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return_full_text=True, |
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repetition_penalty=1.0 |
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) |
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self.sampling_params = dict(do_sample=True, temperature=0.5, top_k=50, top_p=0.9) |
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self.system_prompt = "Sen yardımcı bir asistansın. Sana verilen talimat ve girdilere en uygun cevapları üreteceksin. \n\n\n" |
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def format_prompt(self, user_input): |
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return "[INST] " + self.system_prompt + user_input + " [/INST]" |
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def generate_response(self, user_query): |
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prompt = self.format_prompt(user_query) |
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outputs = self.pipe(prompt, **self.sampling_params) |
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return outputs[0]["generated_text"].split("[/INST]")[1].strip() |
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assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM") |
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# Enter your query here. |
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user_query = "Faiz oranı yükseldiğinde kredi maliyetim nasıl etkilenir?" |
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response = assistant.generate_response(user_query) |
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print(response) |
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``` |
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### Chat Template |
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```python |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "Commencis/Commencis-LLM" |
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messages = [{"role": "user", "content": "Faiz oranı yükseldiğinde kredi maliyetim nasıl etkilenir?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.5, top_k=50, top_p=0.9) |
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print (outputs[0]["generated_text"].split("[/INST]")[1].strip()) |
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``` |
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# Quantized Models: |
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GGUF: https://huggingface.co/Commencis/Commencis-LLM-GGUF |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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Like all LLMs, Commencis-LLM has certain limitations: |
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- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information. |
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- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output. |
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- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses. |
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- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited. |
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- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content. |
