114 lines
4.6 KiB
Markdown
114 lines
4.6 KiB
Markdown
---
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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. |