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Model: ajibawa-2023/OpenHermes-2.5-Code-290k-13B
Source: Original Platform

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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- code
+- finetune
+- synthetic data
+- text-generation-inference
+- conversational
+datasets:
+- ajibawa-2023/OpenHermes-2.5-Code-290k
+- teknium/OpenHermes-2.5
+model-index:
+- name: OpenHermes-2.5-Code-290k-13B
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 57.34
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 80.48
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 56.53
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 52.5
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 74.82
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 58.3
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
+      name: Open LLM Leaderboard
+---
+
+**OpenHermes-2.5-Code-290k-13B**
+
+OpenHermes-2.5-Code-290k-13B is a state of the art Llama-2 Fine-tune, which is trained on additional code dataset.
+This Model is much better than teknium's [model](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B). You can check the **Eval results** below.
+This model is trained on my existing dataset [OpenHermes-2.5-Code-290k](https://huggingface.co/datasets/ajibawa-2023/OpenHermes-2.5-Code-290k). 
+This dataset is amalgamation of two datasets. I have used [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) a super quality dataset made avaliable by teknium. Other datset is my own [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT). 
+Dataset is in Vicuna/ShareGPT format. There are around **1.29 million** set of conversations. I have cleaned the dataset provided by Teknium and removed metadata such as "source" & "category" etc. This dataset has primarily synthetically generated instruction and chat samples. 
+
+This model has enhanced coding capabilities besides other capabilities such as **Blogging, story generation, Q&A and many more**.
+
+**Training:**
+
+Entire model was trained on 4 x A100 80GB. For 2 epoch, training took **21 Days**. Fschat & DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.
+
+
+This is a full fine tuned model. Links for quantized models will be updated soon.
+
+
+**GPTQ, GGUF, AWQ & Exllama**
+
+GPTQ: TBA
+
+GGUF: [Link](https://huggingface.co/LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF)
+
+AWQ: TBA
+
+Exllama v2: [Link](https://huggingface.co/bartowski/OpenHermes-2.5-Code-290k-13B-exl2)
+
+Special Thanks to [LoneStriker](https://huggingface.co/LoneStriker) and [bartowski](https://huggingface.co/bartowski/) for quantising.
+
+
+
+**Example Prompt:**
+```
+This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation. It can generate Story, Blogs .....
+
+Context
+You are a helpful AI assistant.
+
+USER: <prompt>
+ASSISTANT:
+```
+
+You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .
+
+I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
+
+Thank you for your love & support.
+
+**Example Output**
+
+I will update soon.
+
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |63.33|
+|AI2 Reasoning Challenge (25-Shot)|57.34|
+|HellaSwag (10-Shot)              |80.48|
+|MMLU (5-Shot)                    |56.53|
+|TruthfulQA (0-shot)              |52.50|
+|Winogrande (5-shot)              |74.82|
+|GSM8k (5-shot)                   |58.30|

config.json

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+{
+  "_name_or_path": "/media/mla100eus1/Projects/Open-code/",
+  "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

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+  "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"
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special_tokens_map.json

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+    "normalized": false,
+    "rstrip": false,
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+    "normalized": false,
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+    "single_word": false
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+}

tokenizer_config.json

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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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