Update README.md

README.md

@@ -9,6 +9,9 @@ tags:
 - llama
 - trl
 base_model: unsloth/llama-3-8b-bnb-4bit
+datasets:
+- adeocybersecurity/DockerCommand
+pipeline_tag: text-generation
 ---
 
 # Uploaded  model
@@ -17,6 +20,36 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
 
+## Model Details
+
+This model is finetuned on [adeocybersecurity/DockerCommand](https://huggingface.co/datasets/adeocybersecurity/DockerCommand) dataset using the base [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) model. These are only the lora adapaters of the model, the base model is automatically downloaded.
+
+## How to use
+
+```
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+  model_name = "llama-3-docker-command-lora", # YOUR MODEL YOU USED FOR TRAINING
+  max_seq_length = max_seq_length,
+  dtype = dtype,
+  load_in_4bit = load_in_4bit,
+)
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "translate this sentence in docker command.", # instruction
+        "Give me a list of all containers, indicating their status as well.", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+
+```
+
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)