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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+version https://git-lfs.github.com/spec/v1
+oid sha256:d348d189011539f0e36e32503fb33fb62283b8800bd54462d859e1eef6c1ff0f
+size 67201357