Update README.md

README.md

@@ -5,9 +5,9 @@ language:
 ---
 # weblab-10b-instruction-sft-GPTQ 
 
-original model [weblab-10b-instruction-sft](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) which is a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
+Original model [weblab-10b-instruction-sft](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) which is a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
 
-This model is A quantized(miniaturized) version of the original model(21.42GB).
+This model is a quantized(miniaturized) version of the original model(21.42GB).
 
 There are currently two well-known quantization version of original model.  
 (1)GPTQ version(This model. 6.3 GB)  
@@ -23,7 +23,12 @@ But maybe gguf model little bit slower then GPTQ especialy long text.
 
 
 ### sample code
- 
+
+Currently, models may behave differently on local PC and Colab. On Colab, the model may not respond if you include instructional prompts.  
+[Colab Sample script](https://github.com/webbigdata-jp/python_sample/blob/main/weblab_10b_instruction_sft_GPTQ_sample.ipynb)  
+
+If you get an error (something not found or something is not defined) in the script below, please refer to the official documentation and Colab samples and specify a specific version.
+
 ```
 pip install auto-gptq
 ```
@@ -52,13 +57,13 @@ output = model.generate(input_ids=tokens, max_new_tokens=100, do_sample=True, te
 print(tokenizer.decode(output[0]))
 ```
 
-### Other documents
+### Other AutoGPTQ documents
 https://github.com/PanQiWei/AutoGPTQ/blob/main/docs/tutorial/01-Quick-Start.md
 
 ### Benchmark
 
 The results below are preliminary. The blank part is under measurement.  
-Also, the score may change as a result of tuning after this.
+Also, the score may change as a result of more tuning.
 
 * **Japanese benchmark**