Create README.md

README.md

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+---
+license: llama2
+datasets:
+- uonlp/CulturaX
+language:
+- tr
+- en
+pipeline_tag: text-generation
+metrics:
+- accuracy
+- bleu
+---
+
+
+
+# Commencis-LLM
+
+<!-- Provide a quick summary of what the model is/does. -->
+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.
+## Model Description
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [Commencis](https://www.commencis.com)
+- **Language(s):** Turkish
+- **Finetuned from model:** [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- **Input:** Model input text only
+- **Output:** Model generates text only
+- **Blog Post**: 
+
+## Training Details
+Alignment phase consists of two stages: supervised fine-tuning (SFT) and Reward Modeling with Reinforcement learning from human feedback (RLHF).
+
+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.
+
+### Usage
+
+### Suggested Inference Parameters
+- Temperature: 0.5
+- Repetition penalty: 1.0
+- Top-p: 0.9
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+class TextGenerationAssistant:
+    def __init__(self, model_id:str):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+        self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', load_in_8bit=True)
+        self.pipe = pipeline("text-generation", 
+                             model=self.model, 
+                             tokenizer=self.tokenizer,
+                             device_map="auto",
+                             max_new_tokens=1024, 
+                             return_full_text=True,
+                             repetition_penalty=1.0
+                            )
+
+        self.sampling_params = dict(do_sample=True, temperature=0.5, top_k=50, top_p=0.9)
+        self.SYSTEM_PROMPT = "Sen yardımcı bir asistansın. Sana verilen talimat ve girdilere en uygun cevapları üreteceksin. \n\n\n"
+
+    def format_prompt(self, user_input):
+        return "[INST] " + self.SYSTEM_PROMPT + user_input + " [/INST]"
+
+    def generate_response(self, user_query):
+        prompt = self.format_prompt(user_query)
+        outputs = self.pipe(prompt, **self.sampling_params)
+        return outputs[0]["generated_text"].split("[/INST]")[-1]
+
+
+assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM")
+
+# Enter your query here.
+user_query = "Faiz oranları yükseldiğinde kredilerim nasıl etkilenir?"
+response = assistant.generate_response(user_query)
+print(response)
+
+```
+
+### Chat Template
+
+```python
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Commencis/Commencis-LLM"
+messages = [{"role": "user", "content": "Faiz oranları yükseldiğinde kredilerim nasıl etkilenir?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.5, top_k=50, top_p=0.9)
+print(outputs[0]["generated_text"])
+```
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Like all LLMs, Commencis-LLM has certain limitations:
+- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
+- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.
+- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.
+- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
+- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.