Update README.md

README.md

@@ -1,31 +1,59 @@
 ---
 license: llama2
 base_model: TheBloke/Xwin-LM-7B-V0.1-GPTQ
-tags:
-- generated_from_trainer
 model-index:
 - name: cleante
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Cleante
 
-# cleante
+Cleante is a fine-tuned model, based on a pre-trained [7B](https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ) model.
 
-This model is a fine-tuned version of [TheBloke/Xwin-LM-7B-V0.1-GPTQ](https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ) on an unknown dataset.
+## Usage
+
+```python
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+model_name = "guillaumephd/cleante"
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define the text generation pipeline
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0  # Use GPU if available please
+)
+
+# Generate text using the Cleante model
+prompt = "###Human: What's your nickname, assistant? ###Assistant: "
+output = generator(prompt, max_length=100, do_sample=True, temperature=0.5, repetition_penalty=1.2,)
+
+# Print the generated text
+print(output[0]["generated_text"])
+
+outputs = model.generate(**inputs, generation_config=generation_config)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+# The model should output a text that looks like:
+# "My name is Cléante, and I was trained by Guillaume as a language model."
+```
 
 ## Model description
 
-More information needed
+See above.
 
 ## Intended uses & limitations
 
-More information needed
+Demonstration purpose only.
 
 ## Training and evaluation data
 
-More information needed
+Personal data.
 
 ## Training procedure
 
@@ -35,14 +63,8 @@ The following hyperparameters were used during training:
 - learning_rate: 0.0002
 - train_batch_size: 8
 - eval_batch_size: 8
-- seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
-- training_steps: 250
-
-### Training results
-
-
 
 ### Framework versions