Update README.md

README.md

@@ -8,7 +8,9 @@ tags:
 - trl
 license: apache-2.0
 language:
-- en
+- ja
+datasets:
+- kinokokoro/ichikara-instruction-003
 ---
 
 # Uploaded  model
@@ -34,5 +36,58 @@ Training epoch:1
 Authors
 tsuchida rikuto
 
+How to Use
+To use this model, run the code below
+```python
+!pip install -U bitsandbytes
+!pip install -U transformers
+!pip install -U accelerate
+!pip install -U datasets
 
+!pip install ipywidgets --upgrade
 
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+import torch
+from tqdm import tqdm
+import json
+
+
+model_name = "trikudayodayodayo/llm-jp-3-13b-it-1209_lora"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=False,
+)
+
+HF_TOKEN="Type your HF_TOKEN"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token = HF_TOKEN
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token = HF_TOKEN)
+
+input = "Type text here"
+
+tokenized_input = tokenizer.encode(input, add_special_tokens=False, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(
+        tokenized_input,
+        max_new_tokens=100,
+        do_sample=False,
+        repetition_penalty=1.2
+    )[0]
+
+output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+
+print(output)
+```