Quant for 8.0

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -20,64 +20,173 @@ language:
 - en
 library_name: transformers
 pipeline_tag: text-generation
-quantized_by: bartowski
 ---
+<!-- 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. -->
 
-## Exllama v2 Quantizations of gemma-7b-openhermes
+# gemma-7b-openhermes
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/mh-NUO_aNbQpD_NAuFv7g.jpeg)
 
-Original model: https://huggingface.co/abideen/gemma-7b-openhermes
+gemma-7b-openhermes is a variant of the Gemma 7B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset 
+using QLoRA.
 
-No GQA - VRAM requirements will be higher
 
-| Branch                                                         | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
-| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
-| [8_0](https://huggingface.co/bartowski/gemma-7b-openhermes-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
-| [6_5](https://huggingface.co/bartowski/gemma-7b-openhermes-exl2/tree/6_5) | 6.5  | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
-| [5_0](https://huggingface.co/bartowski/gemma-7b-openhermes-exl2/tree/5_0) | 5.0  | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
-| [4_25](https://huggingface.co/bartowski/gemma-7b-openhermes-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
-| [3_5](https://huggingface.co/bartowski/gemma-7b-openhermes-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
+* [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
+* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
 
-## Download instructions
+</details><br>
 
-With git:
+## Usage
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/gemma-7b-openhermes-exl2 gemma-7b-openhermes-exl2-6_5
-```
+### 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-7b-openhermes"
+dtype = torch.bfloat16
 
-With huggingface hub (credit to TheBloke for instructions):
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,
+)
 
-```shell
-pip3 install huggingface-hub
+chat = [{ "role": "user", "content": "What is a Language Model?" }]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `gemma-7b-openhermes-exl2`:
+After the prompt is ready, generation can be performed like this:
 
-```shell
-mkdir gemma-7b-openhermes-exl2
-huggingface-cli download bartowski/gemma-7b-openhermes-exl2 --local-dir gemma-7b-openhermes-exl2 --local-dir-use-symlinks False
+```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]))
 ```
 
-To download from a different branch, add the `--revision` parameter:
+### Inputs and outputs
 
-Linux:
+*   **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.
 
-```shell
-mkdir gemma-7b-openhermes-exl2-6_5
-huggingface-cli download bartowski/gemma-7b-openhermes-exl2 --revision 6_5 --local-dir gemma-7b-openhermes-exl2-6_5 --local-dir-use-symlinks False
-```
+## 🏆 Evaluation results
+
+# Nous Benchmark
+
+
+
+### Training hyperparameters
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+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: 1000
 
-```shell
-mkdir gemma-7b-openhermes-exl2-6.5
-huggingface-cli download bartowski/gemma-7b-openhermes-exl2 --revision 6_5 --local-dir gemma-7b-openhermes-exl2-6.5 --local-dir-use-symlinks False
+
+### 📝 Axolotl Configuration
+
+```yaml
+base_model: google/gemma-7b-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: 1000
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
 ```
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+
+### 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

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "google/gemma-7b-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": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 24576,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 16,
+  "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

@@ -0,0 +1,7 @@
+{
+  "_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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