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README.md

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+---
+license: apache-2.0
+datasets:
+- uonlp/CulturaX
+language:
+- tr
+- en
+pipeline_tag: text-generation
+metrics:
+- accuracy
+- bleu
+base_model: mistralai/Mistral-7B-Instruct-v0.1
+---
+
+
+
+# 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-v0.1)
+- **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/)
+
+## 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,load_in_4bit=False)
+        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].strip()
+
+
+assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM")
+
+# Enter your query here.
+user_query = "Faiz oranı yükseldiğinde kredi maliyetim 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 oranı yükseldiğinde kredi maliyetim 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"].split("[/INST]")[1].strip())
+```
+
+# Quantized Models:
+
+GGUF: https://huggingface.co/Commencis/Commencis-LLM-GGUF
+
+## 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.

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.38.2"
+}

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