
**Requirements:**

You can run this model on Google Colab Pro, it requires a substantial amount of VRAM.

<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 --&gt;: ", 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>(<span style="color: #A31515;">"\n\n"</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” --&gt;: Multiple Regression for Appraisal (MRA) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates)
</code>
</pre>

{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["quantization ", "LLM", "Dolly"]}
transformers quantization  LLM Dolly en license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #quantization  #LLM #Dolly #en #license-apache-2.0 #endpoints_compatible #region-us 


Requirements:

You can run this model on Google Colab Pro, it requires a substantial amount of VRAM.

<pre>
!pip install -q -U bitsandbytes
!pip install -q -U git+URL 
!pip install -q -U git+URL
!pip install -q -U git+URL
</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 --&gt;: ", return_tensors=<span style="color: #A31515;">'pt'</span>)
<span style="color: #0000FF;">with</span> URL.autocast():
    output_tokens = model.generate(batch, max_new_tokens=<span style="color: #098658;">50</span>)

<span style="color: #0000FF;">print</span>(<span style="color: #A31515;">"\n\n"</span>, URL(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” --&gt;: Multiple Regression for Appraisal (MRA) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates)
</code>
</pre>

# 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 --&gt;: ", return_tensors=<span style="color: #A31515;">'pt'</span>)
<span style="color: #0000FF;">with</span> URL.autocast():
    output_tokens = model.generate(batch, max_new_tokens=<span style="color: #098658;">50</span>)

<span style="color: #0000FF;">print</span>(<span style="color: #A31515;">"\n\n"</span>, URL(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” --&gt;: Multiple Regression for Appraisal (MRA) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates)
</code>
</pre>
TAGS
#transformers #quantization  #LLM #Dolly #en #license-apache-2.0 #endpoints_compatible #region-us 
 # 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 --&gt;: ", return_tensors=<span style="color: #A31515;">'pt'</span>)
<span style="color: #0000FF;">with</span> URL.autocast():
    output_tokens = model.generate(batch, max_new_tokens=<span style="color: #098658;">50</span>)

<span style="color: #0000FF;">print</span>(<span style="color: #A31515;">"\n\n"</span>, URL(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” --&gt;: Multiple Regression for Appraisal (MRA) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates) --&gt;: Multiple Regression for Appraisal (MRA) (with Covariates)
</code>
</pre>