Quant for 5.0

README.md

@@ -6,60 +6,54 @@ language:
 - en
 tags:
 - code
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
 
-## Exllama v2 Quantizations of Code-290k-13B
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
+**Code-290k-13B**
 
-# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
+Large Language Models (LLMs) are good with code generations. Sometimes they do make mistakes in code generation. How about if they can give detailed explanation along with the code.
+This is what I have tried over here. The base Llama-2 model was used for training purpose. It is trained on around **290000** set of codes. Each set having 2 conversations.
+Along with Python, Java, JavaScript, GO, C++, Rust, Ruby, Sql, MySql, R, Julia, Haskell, etc. code with detailed explanation is used for training purpose. It is built upon using my existing Datasets [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT) and [Code-74k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-74k-ShareGPT) .
+This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. 
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+I have released the new data [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) on which this Model is trained.
 
-Conversion was done using the default calibration dataset.
+**Training:**
 
-Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
+Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 165 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.
 
-Original model: https://huggingface.co/ajibawa-2023/Code-290k-13B
 
+This is a full fine tuned model. Links for quantized models will be updated soon.
 
 
-<a href="https://huggingface.co/bartowski/Code-290k-13B-exl2/tree/6_5">6.5 bits per weight</a>
+**GPTQ GGUF & AWQ**
 
-<a href="https://huggingface.co/bartowski/Code-290k-13B-exl2/tree/5_0">5.0 bits per weight</a>
+GPTQ: TBA
 
-<a href="https://huggingface.co/bartowski/Code-290k-13B-exl2/tree/4_0">4.0 bits per weight</a>
+GGUF: TBA
 
-<a href="https://huggingface.co/bartowski/Code-290k-13B-exl2/tree/3_75">3.75 bits per weight</a>
+AWQ: TBA
 
-<a href="https://huggingface.co/bartowski/Code-290k-13B-exl2/tree/3_0">3.0 bits per weight</a>
 
-## Download instructions
 
-With git:
 
-```shell
-git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/Code-290k-13B-exl2
+**Example Prompt:**
 ```
+This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation.
 
-With huggingface hub (credit to TheBloke for instructions):
+Context
+You are a helpful AI assistant.
 
-```shell
-pip3 install huggingface-hub
+USER: <prompt>
+ASSISTANT:
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Code-290k-13B-exl2`:
+You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .
 
-```shell
-mkdir Code-290k-13B-exl2
-huggingface-cli download bartowski/Code-290k-13B-exl2 --local-dir Code-290k-13B-exl2 --local-dir-use-symlinks False
-```
+I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
 
-To download from a different branch, add the `--revision` parameter:
+Thank you for your love & support.
 
-```shell
-mkdir Code-290k-13B-exl2
-huggingface-cli download bartowski/Code-290k-13B-exl2 --revision 4_0 --local-dir Code-290k-13B-exl2 --local-dir-use-symlinks False
-```
+**Example Output**
+
+Will update soon.

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "/media/mla100scus/Projects_1/Code-290k-13B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.28.1",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.28.1"
+}

original_repo_url.txt

@@ -0,0 +1 @@
+https://huggingface.co/ajibawa-2023/Code-290k-13B

output.safetensors

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+size 8395244694

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