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Model: rasyosef/Llama-3.1-Minitron-4B-Chat
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
+datasets:
+- teknium/OpenHermes-2.5
+pipeline_tag: text-generation
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+---
+
+# Llama-3.1-Minitron-4B-Chat
+
+This is an instruction-tuned version of [nvidia/Llama-3.1-Minitron-4B-Width-Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base) that has underwent **supervised fine-tuning** with 64k instruction-response pairs from the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) dataset on a single **A100 40GB** GPU.
+
+## How to use
+### Chat Format
+
+Given the nature of the training data, the Llama-3.1-Minitron-4B chat model is best suited for prompts using the chat format as follows. 
+You can provide the prompt as a question with a generic template as follows:
+```markdown
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+Question?<|im_end|>
+<|im_start|>assistant
+```
+
+For example:
+```markdown
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+How to explain Internet for a medieval knight?<|im_end|>
+<|im_start|>assistant
+```
+where the model generates the text after `<|im_start|>assistant` .
+
+### Sample inference code
+
+Support for this model will be added in the upcoming transformers release. In the meantime, please **install the library from source**:
+
+```
+pip install git+https://github.com/huggingface/transformers
+```
+
+This code snippets show how to get quickly started with running the model on a GPU:
+
+```python
+import torch 
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 
+
+torch.random.manual_seed(0) 
+
+model_id = "rasyosef/Llama-3.1-Minitron-4B-Chat"
+model = AutoModelForCausalLM.from_pretrained( 
+    model_id,  
+    device_map="auto",  
+    torch_dtype=torch.bfloat16 
+) 
+
+tokenizer = AutoTokenizer.from_pretrained(model_id) 
+
+messages = [ 
+    {"role": "system", "content": "You are a helpful AI assistant."}, 
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, 
+] 
+
+pipe = pipeline( 
+    "text-generation", 
+    model=model, 
+    tokenizer=tokenizer, 
+) 
+
+generation_args = { 
+    "max_new_tokens": 256, 
+    "return_full_text": False, 
+    "temperature": 0.0, 
+    "do_sample": False, 
+} 
+
+output = pipe(messages, **generation_args) 
+print(output[0]['generated_text'])  
+```
+
+Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_