Update README.md

README.md

@@ -10,14 +10,93 @@ tags:
 - trl
 - sft
 base_model: unsloth/mistral-7b-bnb-4bit
+
 ---
 
-# Uploaded  model
+# Nepali GPT
+Nepali GPT is a large Nepali language fine-tuned model based on Mixtral_7B.The fine-tuning process uses Unsloth, expediting the training process for optimal efficiency.
+
+
+
+## Model Description
+* Model type: A 7B fine-tuned model
+* Primary Language(s): Nepali
+* License: Mistral
+
+
+### Installation
+```
+#Install Unsloth
+%%capture
+import torch
+major_version, minor_version = torch.cuda.get_device_capability()
+# Must install separately since Colab has torch 2.2.1, which breaks packages
+!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+if major_version >= 8:
+    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
+    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
+else:
+    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)
+    !pip install --no-deps xformers trl peft accelerate bitsandbytes
+pass
+```
+
+### Model loading
+```
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Heem2/NEPALIGPT-1.0",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+
+prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+```
+### Inference
+```
+FastLanguageModel.for_inference(model)
+inputs = tokenizer(
+[
+    prompt.format(
+        "नेपालको बारेमा व्याख्या गर्नुहोस्।?", # instruction
+        "संस्कृति, भाषा, भूगोल, राजनीति, जलवायु", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 1000, use_cache = True)
+tokenizer.batch_decode(outputs)
+
+
+```
+
+### Citation Information
+
+If you find this model useful, please consider giving 👏 and citing:
+
+```
+@heem2
+}
+```
+
+### Contributions
 
-- **Developed by:** Heem2
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
+- This is developed by Hem Bahadur Gurung.Feel free to DM  if you have any questions.
 
-This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)