Add bengali lora model

README.md

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 ---
 license: other
 ---
+# Hugging Face Model - Bengali Finetuned
+
+This repository contains a Hugging Face model that has been fine-tuned on a Bengali dataset. The model uses the `peft` library for generating responses.
+
+## Usage
+
+To use the model, first import the necessary libraries:
+
+```python
+from peft import PeftModel
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+```
+
+Next, load the tokenizer and model:
+
+```python
+tokenizer = LlamaTokenizer.from_pretrained("yahma/llama-7b-hf")
+model = LlamaForCausalLM.from_pretrained(
+ "yahma/llama-7b-hf",
+ load_in_8bit=True,
+ device_map="auto",
+)
+```
+
+Then, load the `PeftModel` with the specified pre-trained model and path to the peft model:
+
+```python
+model = PeftModel.from_pretrained(model, "./bengali-dolly-alpaca-lora-7b")
+```
+
+Next, define a function to generate a prompt:
+
+```python
+def generate_prompt(instruction, input=None):
+ if input:
+ return f"""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:
+{instruction}
+
+### Input:
+{input}
+
+### Response:"""
+ else:
+ return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:"""
+```
+
+Finally, define a function to evaluate the model:
+
+```python
+generation_config = GenerationConfig(
+ temperature=0.1,
+ top_p=0.75,
+ num_beams=4,
+)
+
+def evaluate(model, instruction, input=None):
+ prompt = generate_prompt(instruction, input)
+ inputs = tokenizer(prompt, return_tensors="pt")
+ input_ids = inputs["input_ids"].cuda()
+ generation_output = model.generate(
+ input_ids=input_ids,
+ generation_config=generation_config,
+ return_dict_in_generate=True,
+ output_scores=True,
+ max_new_tokens=256
+ )
+ for s in generation_output.sequences:
+ output = tokenizer.decode(s)
+ print("Response:", output.split("### Response:")[1].strip())
+
+instruct =input("Instruction: ")
+evaluate(model, instruct)
+```
+
+To generate a response, simply run the `evaluate` function with an instruction and optional input:
+
+```python
+instruct = "Write a response that appropriately completes the request."
+input = "This is a sample input."
+evaluate(model, instruct, input)
+```
+
+This will output a response that completes the request.

adapter_config.json

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+{
+ "base_model_name_or_path": "yahma/llama-7b-hf",
+ "bias": "none",
+ "enable_lora": null,
+ "fan_in_fan_out": false,
+ "inference_mode": true,
+ "init_lora_weights": true,
+ "lora_alpha": 16,
+ "lora_dropout": 0.05,
+ "merge_weights": false,
+ "modules_to_save": null,
+ "peft_type": "LORA",
+ "r": 16,
+ "target_modules": [
+ "q_proj",
+ "k_proj",
+ "v_proj",
+ "o_proj"
+ ],
+ "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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+size 67201357