|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
library_name: transformers |
|
tags: |
|
- 'quantization ' |
|
- LLM |
|
- Dolly |
|
--- |
|
|
|
Requirements: |
|
|
|
<pre> |
|
!pip install -q -U bitsandbytes |
|
!pip install -q -U git+https://github.com/huggingface/transformers.git |
|
!pip install -q -U git+https://github.com/huggingface/peft.git |
|
!pip install -q -U git+https://github.com/huggingface/accelerate.git |
|
</pre> |
|
|
|
Import this model using: |
|
|
|
<pre> |
|
<code> |
|
<span style="color: #0000FF;">import</span> torch |
|
<span style="color: #0000FF;">from</span> peft <span style="color: #0000FF;">import</span> PeftModel, PeftConfig |
|
<span style="color: #0000FF;">from</span> transformers <span style="color: #0000FF;">import</span> AutoModelForCausalLM, AutoTokenizer |
|
|
|
peft_model_id = "<span style="color: #A31515;>"AhmedBou/databricks-dolly-v2-3b_on_NCSS"</span> |
|
config = PeftConfig.from_pretrained(peft_model_id) |
|
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=<span style="color: #0000FF;">True</span>, load_in_8bit=<span style="color: #0000FF;">True</span>, device_map=<span style="color: #0000FF;">'auto'</span>) |
|
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
|
|
|
<span style="color: #808080;"># Load the Lora model</span> |
|
model = PeftModel.from_pretrained(model, peft_model_id) |
|
</code> |
|
</pre> |
|
|
|
|
|
Inference using: |
|
|
|
<pre> |
|
<code> |
|
<span style="color: #0000FF;">batch</span> = tokenizer("Multiple Regression for Appraisal -->: ", return_tensors=<span style="color: #A31515;">'pt'</span>) |
|
|
|
<span style="color: #0000FF;">with</span> torch.cuda.amp.autocast(): |
|
output_tokens = model.generate(**batch, max_new_tokens=<span style="color: #098658;">50</span>) |
|
|
|
<span style="color: #0000FF;">print</span>(' |
|
', tokenizer.decode(output_tokens[<span style="color: #098658;">0</span>], skip_special_tokens=<span style="color: #0000FF;">True</span>)) |
|
</code> |
|
</pre> |
|
|
|
|
|
Output: |
|
|
|
<pre> |
|
<code> |
|
“Multiple Regression for Appraisal” -->: Multiple Regression for Appraisal (MRA) -->: Multiple Regression for Appraisal (MRA) (with Covariates) -->: Multiple Regression for Appraisal (MRA) (with Covariates) |
|
</code> |
|
</pre> |
|
|