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README.md

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+---
+task_categories:
+- text-generation
+---
+# Description
+This language model is the version 0.0 of a Gradio Coding Assistant. It is an instruction fine-tuned version of [StarCoder](https://huggingface.co/bigcode/starcoder) that is
+designed to provide assistance to developers who use [gradio](https://www.gradio.app).
+
+# Dataset
+The dataset is multi-source. Its content comes from the following sources
+- The stack
+
+More precisely, we looked into [the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup) which contain codes permissive licenses. We shortlisted the files whose
+content incorporated the keyword `gradio`.
+- GitHub Issues
+
+We scrapped all the issues of the official repository [the-gradio-app/gradio](https://github.com/gradio-app/gradio) and added them to our training dataset.
+- Spaces on Hugging Face Hub
+
+We used the [HuggingFace_Hub API](https://huggingface.co/docs/huggingface_hub/package_reference/hf_api) to scrape the data from the spaces which are designed with gradio. We kept track of those
+with permissive licenses, namely MIT and Apache 2.0. This set of code was further deduplicated.
+
+# Training setting and hyperparameters
+For our fine-tuning, we decided to follow a 2-step strategy.
+- Pretraining (Fine-tuning) with next token prediction on the previously built gradio dataset (this step should familiarize the model with the gradio syntax.).
+- Instruction fine-tuning on an instruction dataset (this step should make the model conversational.).
+For both steps, we made use of parameter-efficient fine-tuning via the library [PEFT](https://github.com/huggingface/peft), more precisely [LoRA](https://arxiv.org/abs/2106.09685). Our
+training script is the famous [starcoder fine-tuning script](https://github.com/bigcode-project/starcoder).
+
+## Resources
+Our training was done of 8 A100 GPUs of 80GB.
+
+## Pretraining
+These are the parameters that we used :
+- learning rate : 5e-4
+- warmup_steps :
+- gradient_accumulation_steps : 4
+- batch_size : 1
+- sequence length : 2048
+- max_steps : 1000
+- warmup_steps : 5
+- weight_decay : 0.05
+- learning rate scheduler : cosine
+
+**LORA PARAMETERS** :
+- r = 16
+- alpha = 32
+- dropout = 0.05
+
+We stopped the training before the end and kept the *checkpoint-100* for the second step.
+
+## Fine-tuning
+This step consisted into the instruction fine-tuning of the previous checkpoint. For that purpose, we used a modified version of [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
+The template for the instruction fine-tuning was `Question: {question}\n\nAnswer: {answer}`. We used exactly the same parameters we used during the pretraining and we kept the *checkpoint-50*.
+
+# Usage
+The usage is straightforward and very similar to any other instruction fine-tuned model.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint_name="ArmelR/starcoder-gradio-v0"
+model = AutoModelForCausalLM.from_pretrained(checkpoint_name)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_name)
+
+prompt = "Create a gradio application that help to convert temperature in celcius into temperature in Fahrenheit"
+inputs = tokenizer(f"Question: {prompt}\n\nAnswer: ", return_tensors="pt")
+
+outputs = model.generate(
+  inputs["input_ids"],
+  temperature=0.2,
+  top_p=0.95,
+  max_new_tokens=200
+)
+
+input_len=len(inputs["input_ids"])
+print(tokenizer.decode(outputs[0][input_len:]))
+```
+# Updates
+Gradio dataset `.filter(lambda x : ("gradio" in x["content"] or "gr." in x["content"]) and "streamlit" not in x["content"]`)
+Guanaco `ArmelR/oasst1_guanaco`
+- StarCoderbase (950, 1350)
+  - max_steps = 2000
+  - shuffle_buffer = 100
+  - batch_size = 2
+  - gradient_accumulation_steps = 4
+  - num_warmup_steps = 100
+  - weight_decay = 0.01
+- StarCoderplus (2000)
+
+Guanaco multi-turn (HuggingFaceH4/oasst1_en)
+# More information
+For further information, refer to [StarCoder](https://huggingface.co/bigcode/starcoder).

config.json

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+{
+  "_name_or_path": "bigcode/starcoderplus",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPTBigCodeForCausalLM"
+  ],
+  "attention_softmax_in_fp32": true,
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 0,
+  "inference_runner": 0,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_batch_size": null,
+  "max_sequence_length": null,
+  "model_type": "gpt_bigcode",
+  "multi_query": true,
+  "n_embd": 6144,
+  "n_head": 48,
+  "n_inner": 24576,
+  "n_layer": 40,
+  "n_positions": 8192,
+  "pad_key_length": true,
+  "pre_allocate_kv_cache": false,
+  "resid_pdrop": 0.1,
+  "scale_attention_softmax_in_fp32": true,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "validate_runner_input": true,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.31.0"
+}

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+}

special_tokens_map.json

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+{
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+}

tokenizer_config.json

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+  "vocab_size": 49152
+}