Upload README.md with huggingface_hub

README.md

@@ -1,8 +1,10 @@
+
 ---
+
 license: apache-2.0
 datasets:
 - JetBrains/KExercises
-base_model: JetBrains/deepseek-coder-1.3B-kexer
+base_model: deepseek-ai/deepseek-coder-1.3b-base
 results:
 - task:
     type: text-generation
@@ -15,12 +17,18 @@ results:
     value: 36.65
 tags:
 - code
-pipeline_tag: text-generation
+
 ---
 
-# Deepseek-Coder-1.3B-kexer-GGUF
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/deepseek-coder-1.3B-kexer-GGUF
 This is quantized version of [JetBrains/deepseek-coder-1.3B-kexer](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) created using llama.cpp
 
+# Original Model Card
+
+
 # Kexer models
 
 Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. 
@@ -28,6 +36,34 @@ This is a repository for the fine-tuned **Deepseek-coder-1.3b** model in the *Hu
 
 # How to use
 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = 'JetBrains/deepseek-coder-1.3B-kexer'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
+
+# Create and encode input
+input_text = """\
+This function takes an integer n and returns factorial of a number:
+fun factorial(n: Int): Int {\
+"""
+input_ids = tokenizer.encode(
+    input_text, return_tensors='pt'
+).to('cuda')
+
+# Generate
+output = model.generate(
+    input_ids, max_length=60, num_return_sequences=1, 
+    early_stopping=True, pad_token_id=tokenizer.eos_token_id,
+)
+
+# Decode output
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
+
 As with the base model, we can use FIM. To do this, the following format must be used: 
 ```
 '<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>'
@@ -64,4 +100,4 @@ Here are the results of our evaluation:
 
 # Ethical considerations and limitations
 
-Deepseek-coder-1.3B-Kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Deepseek-coder-1.3B-Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Deepseek-coder-1.3B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
+Deepseek-coder-1.3B-Kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Deepseek-coder-1.3B-Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Deepseek-coder-1.3B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.