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Model: abideen/gemma-2b-openhermes
Source: Original Platform

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README.md

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+---
+license: cc-by-nc-4.0
+base_model: google/gemma-2b-it
+tags:
+- generated_from_trainer
+- axolotl
+- gemma
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+model-index:
+- name: gemma-2b-openhermes
+  results: []
+datasets:
+- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+<!-- 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. -->
+
+# gemma-2b-openhermes
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/9bmxL8Lt7hBaKlKHVxtew.jpeg)
+
+gemma-2b-openhermes is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset 
+using QLoRA.
+
+
+* [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
+* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
+
+</details><br>
+
+## Usage
+
+### Chat Template
+
+The instruction-tuned models use a chat template that must be adhered to for conversational use.
+The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
+
+Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model_id = "abideen/gemma-2b-openhermes"
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,
+)
+
+chat = [{ "role": "user", "content": "What is a Language Model?" }]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+```
+
+After the prompt is ready, generation can be performed like this:
+
+```py
+inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=250)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Inputs and outputs
+
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated English-language text in response to the input, such
+    as an answer to a question, or a summary of a document.
+
+## 🏆 Evaluation results
+
+# Nous Benchmark
+
+Agieval
+
+| Task                                      | Version | Metric | Value |   | StdErr |
+|-------------------------------------------|---------|--------|-------|---|---------|
+| agieval\_aqua\_rat                        | 0       | acc    | 24.02 | _ | 2.69    |
+| agieval\_aqua\_rat                        | 0       | acc\_norm | 24.02 | _ | 2.69    |
+| agieval\_logiqa\_en                      | 0       | acc    | 23.20 | _ | 1.66    |
+| agieval\_logiqa\_en                      | 0       | acc\_norm | 24.42 | _ | 1.69    |
+| agieval\_lsat\_ar                        | 0       | acc    | 18.26 | _ | 2.55    |
+| agieval\_lsat\_ar                        | 0       | acc\_norm | 18.70 | _ | 2.58    |
+| agieval\_lsat\_lr                        | 0       | acc    | 22.35 | _ | 1.85    |
+| agieval\_lsat\_lr                        | 0       | acc\_norm | 23.53 | _ | 1.88    |
+| agieval\_lsat\_rc                        | 0       | acc    | 20.82 | _ | 2.48    |
+| agieval\_lsat\_rc                        | 0       | acc\_norm | 20.07 | _ | 2.45    |
+| agieval\_sat\_en                         | 0       | acc    | 32.52 | _ | 3.27    |
+| agieval\_sat\_en                         | 0       | acc\_norm | 32.52 | _ | 3.27    |
+| agieval\_sat\_en\_without\_passage       | 0       | acc    | 25.73 | _ | 3.05    |
+| agieval\_sat\_en\_without\_passage       | 0       | acc\_norm | 24.27 | _ | 2.99    |
+| agieval\_sat\_math                        | 0       | acc    | 25.00 | _ | 2.93    |
+| agieval\_sat\_math                        | 0       | acc\_norm | 20.91 | _ | 2.75    |
+Average: 24.11
+
+GPT4ALL
+
+| Task                 | Version | Metric | Value |   | StdErr |
+|----------------------|---------|--------|-------|---|---------|
+| arc\_challenge       | 0       | acc    | 21.77 | _ | 1.21    |
+| arc\_challenge       | 0       | acc\_norm | 24.15 | _ | 1.25    |
+| arc\_easy            | 0       | acc    | 37.37 | _ | 0.99    |
+| arc\_easy            | 0       | acc\_norm | 36.95 | _ | 0.99    |
+| boolq               | 1       | acc    | 65.60 | _ | 0.83    |
+| hellaswag           | 0       | acc    | 34.54 | _ | 0.47    |
+| hellaswag           | 0       | acc\_norm | 40.54 | _ | 0.49    |
+| openbookqa          | 0       | acc    | 15.00 | _ | 1.59    |
+| openbookqa          | 0       | acc\_norm | 27.40 | _ | 2.00    |
+| piqa                | 0       | acc    | 60.88 | _ | 1.14    |
+| piqa                | 0       | acc\_norm | 60.55 | _ | 1.14    |
+| winogrande          | 0       | acc    | 50.91 | _ | 1.41    |
+Average: 40.01
+
+BigBench
+
+| Task                              | Version | Metric | Value  | Std Err |
+|-----------------------------------|---------|--------|--------|---------|
+| bigbench\_causal\_judgement        | 0       | MCG    | 50     | 2.26   |
+| bigbench\_date\_understanding       | 0       | MCG    | 49.14  | 2.18   |
+| bigbench\_disambiguation\_qa        | 0       | MCG    | 49.31  | 2.74   |
+| bigbench\_geometric\_shapes         | 0       | MCG    | 14.18  | 1.37   |
+| bigbench\_logical\_deduction\_5objs | 0       | MCG    | 49.41  | 2.73   |
+| bigbench\_logical\_deduction\_7objs | 0       | MCG    | 41.48  | 2.46   |
+| bigbench\_logical\_deduction\_3objs | 0       | MCG    | 69.33  | 2.75   |
+| bigbench\_movie\_recommendation     | 0       | MCG    | 51.71  | 2.25   |
+| bigbench\_navigate                 | 0       | MCG    | 50     | 1.58   |
+| bigbench\_reasoning\_colored\_obj   | 0       | MCG    | 51.92  | 0.99   |
+| bigbench\_ruin\_names               | 0       | MCG    | 48.14  | 2.01   |
+| bigbench\_salient\_trans\_err\_detec | 0       | MCG    | 39.92  | 1.2    |
+| bigbench\_snarks                   | 0       | MCG    | 64.14  | 3.71   |
+| bigbench\_sports\_understanding     | 0       | MCG    | 55.31  | 1.59   |
+| bigbench\_temporal\_sequences       | 0       | MCG    | 46.92  | 1.4    |
+| bigbench\_tsk\_shuff\_objs\_5       | 0       | MCG    | 25.04  | 1.01   |
+| bigbench\_tsk\_shuff\_objs\_7       | 0       | MCG    | 15.04  | 0.72   |
+| bigbench\_tsk\_shuff\_objs\_3       | 0       | MCG    | 55.33  | 2.75   |
+Average: 44.75
+
+TruthfulQA
+
+| Task                             | Version | Metric | Value | Std Err |
+|----------------------------------|---------|--------|--------|----------|
+| truthfulqa\_mc                   | 1       | mc1    | 30.11  | 1.61    |
+| truthfulqa\_mc                   | 1       | mc2    | 47.69  | 1.61    |
+Average: 38.90
+
+
+# Openllm Benchmark
+
+|    Task     |Version| Metric |Value|   |Stderr|
+|-------------|------:|--------|----:|---|-----:|
+|arc_challenge|      0|acc     |40.44|±  |  1.43|
+|             |       |acc_norm|43.81|±  |  1.34|
+|hellaswag    |      0|acc     |48.1 |±  |  0.45|
+|             |       |acc_norm|62.73|±  |  0.32|
+|gsm8k        |      0|acc     |5.6  |±  |  0.6 |
+|winogrande   |      0|acc     |60.91|±  |  1.3 |
+|mmlu         |      0|acc     |37.62  |±|  0.6 |
+
+Average: 73.5%
+
+### TruthfulQA
+|    Task     |Version|Metric|Value|   |Stderr|
+|-------------|------:|------|----:|---|-----:|
+|truthfulqa_mc|      1|mc1   |29.00|±  |  1.58|
+|             |       |mc2   |45.83|±  |  1.59|
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-07
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- training_steps: 1300
+
+
+### 📝 Axolotl Configuration
+
+```yaml
+base_model: google/gemma-2b-it
+model_type: GemmaForCausalLM
+tokenizer_type: GemmaTokenizer
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+rl: dpo
+chat_template: chatml
+datasets:
+  - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
+    split: train
+    type: chatml.intel
+dataset_prepared_path:
+val_set_size: 0.01
+output_dir: ./out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 1800
+sample_packing: false
+pad_to_sequence_len: false
+
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+
+wandb_project: gemma
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 5e-7
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: false
+
+warmup_steps: 100
+evals_per_epoch: 1
+eval_table_size:
+eval_table_max_new_tokens: 128
+save_steps: 1000
+max_steps: 1300
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+```
+
+
+### Framework versions
+
+- Transformers 4.39.0.dev0
+- Pytorch 2.1.2+cu118
+- Datasets 2.17.0
+- Tokenizers 0.15.0
+- axolotl: 0.4.0
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

config.json

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+{
+  "_name_or_path": "google/gemma-2b-it",
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.0.dev0",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.39.0.dev0"
+}

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special_tokens_map.json

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+{
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+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05e97791a5e007260de1db7e1692e53150e08cea481e2bf25435553380c147ee
+size 17477929

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6969e64047744a44bb3abfb5c50f8de0f7ed8b571d5444426ef931f651d1a0ef
+size 4241111

tokenizer_config.json

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+{
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+      "single_word": false,
+      "special": true
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+  "use_default_system_prompt": false
+}