Create README.md

README.md

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+---
+license: apache-2.0
+base_model: OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1
+pipeline_tag: text-generation
+---
+
+# QuantFactory/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1-GGUF
+This is quantized version of [OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1](https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1) created using llama.cpp
+
+# Model Description
+Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
+https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
+
+
+We don't know how good this model is exactly in benchmarks since we have not benched this yet, but we think real prompts and usage is more telling anyways. 
+
+
+From our testing this model is:
+
+- Less Refusals
+- More Uncensored
+- Follows requests better
+- Can reply in requested formats better without adding unnecesary information
+
+We are happy for anyone to try it out and give some feedback.
+
+
+Training:
+- 2048 sequence length, while the base model is 8192 sequence length. From testing it still performs the same 8192 context just fine.
+- Trained on a modified and improved version of Cognitive Computations Eric Hartford's Dolphin dataset. https://huggingface.co/datasets/cognitivecomputations/dolphin
+- Training duration is around 2 days on 2x RTX3090 on our own machine, using 4-bit loading and Qlora 64-rank 128-alpha resulting in ~2% trainable weights.
+
+
+The goal for this model is to have the model less-censored and great at general tasks like the previous dolphin based models by Eric Hartford.
+We started training this BEFORE they launched their own full weight trained Llama-3-8B-Dolphin-2.9 with their own curated datasets and the newer "Dolphin 2.9" dataset, but we think this model is still a unique take on Llama 3 8B Instruct and the dolphin dataset.
+https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b
+
+
+The difference with their dolphin 2.9 model is that we train this using Meta's new Llama 3 instruct format and not the regular ChatML format that Dolphin models are usually trained on. 
+This is because we think that it performed better using the format it was originally trained on.
+
+Instruct format:
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+```
+
+
+
+
+[<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)
+
+Axolotl Config:
+```
+base_model: Meta-Llama-3-8B-Instruct
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+  
+train_on_inputs: false
+group_by_length: false
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+sequence_len: 2048
+bf16: true
+fp16: false
+tf32: false
+flash_attention: true
+
+# Data
+datasets:
+  - path: flan1m-universal-uncensored-system-2048.jsonl
+    type:
+      system_prompt: ""
+      system_format: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
+      field_system: system
+      field_instruction: input
+      field_output: output
+      format: "{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+      no_input_format: "{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    
+warmup_steps: 10
+dataset_prepared_path: ./last_run_prepared
+
+# Iterations
+num_epochs: 1
+saves_per_epoch: 4
+
+# Evaluation
+val_set_size: 0.01
+eval_table_size:
+eval_table_max_new_tokens:
+eval_sample_packing: false
+evals_per_epoch: 4
+
+# LoRA
+output_dir: ./qlora-out
+adapter: qlora
+lora_model_dir:
+lora_r: 64
+lora_alpha: 128
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+save_safetensors: true
+
+# Sampling
+sample_packing: true
+pad_to_sequence_len: true
+
+# Batching
+gradient_accumulation_steps: 32
+micro_batch_size: 4
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+
+# Optimizer
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+# Misc
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+debug:
+deepspeed: zero3_bf16.json
+weight_decay: 0.1
+special_tokens:
+   pad_token: <|end_of_text|>
+```