QueryloopAI
/

gemma-2b-openhermes

@@ -1,199 +1,283 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+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)