Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language:
 - en
+- sw
 license: apache-2.0
 tags:
 - text-generation-inference
@@ -20,81 +21,23 @@ base_model: unsloth/gemma-7b-bnb-4bit
 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 
-# Inference With Unsloth on colab
-
-
-```python3
-
-
-import torch
-major_version, minor_version = torch.cuda.get_device_capability()
- 
-
-!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
-
-
-
-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 = False 
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "Mollel/Gemma_Swahili_Mollel_1_epoch",
-    max_seq_length = max_seq_length,
-    dtype = dtype,
-    load_in_4bit = load_in_4bit,
-    device_map="auto"
-)
-FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-
-input_prompt = """
-### Instruction:
-{}
-
-### Input:
-{}
-
-### Response:
-{}"""
-
-input_text = input_prompt.format(
-        "دیئے گئے موضوع کے بارے میں ایک مختصر پیراگراف لکھیں۔", # instruction
-        "قابل تجدید توانائی کے استعمال کی اہمیت", # input
-        "", # output - leave this blank for generation!
-    )
-
-inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
-
-outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
-
-response = tokenizer.batch_decode(outputs)
-
-```
-
 
 
 # Inference With Inference with HuggingFace transformers
 
 
-
-
 ```python3
 
+!pip install transformers peft accelerate bitsandbytes
+
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
 
 model = AutoPeftModelForCausalLM.from_pretrained(
-    "Xhaheen/Gemma_Urdu_Shaheen_1_epoch",
+    "Mollel/Gemma_Swahili_Mollel_1_epoch",
     load_in_4bit = False
 )
+
 tokenizer = AutoTokenizer.from_pretrained("Mollel/Gemma_Swahili_Mollel_1_epoch")
 
 
@@ -111,8 +54,8 @@ input_prompt = """
 
 
 input_text = input_prompt.format(
-        "دیئے گئے موضوع کے بارے میں ایک مختصر پیراگراف لکھیں۔", # instruction
-        "قابل تجدید توانائی کے استعمال کی اہمیت", # input
+        "Andika aya fupi kuhusu mada iliyotolewa.", # instruction
+        "Umuhimu wa kutumia nishati inayoweza kurejeshwa", # input
         "", # output - leave this blank for generation!
     )
 
@@ -121,6 +64,8 @@ inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
 response = tokenizer.batch_decode(outputs)[0]
 
+print(response)
+
 ```
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)