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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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- axolotl |
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- autoquant |
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- gguf |
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base_model: meta-llama/Meta-Llama-3-8B |
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datasets: |
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- cognitivecomputations/Dolphin-2.9 |
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- teknium/OpenHermes-2.5 |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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- cognitivecomputations/dolphin-coder |
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- cognitivecomputations/samantha-data |
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- HuggingFaceH4/ultrachat_200k |
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- microsoft/orca-math-word-problems-200k |
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- abacusai/SystemChat-1.1 |
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- Locutusque/function-calling-chatml |
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- internlm/Agent-FLAN |
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model-index: |
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- name: out |
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results: [] |
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--- |
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# MoMonir/dolphin-2.9-llama3-8b-1m-GGUF |
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This model was converted to GGUF format from [`cognitivecomputations/dolphin-2.9-llama3-8b-1m`](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) |
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Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) for more details on the model. |
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<!-- README_GGUF.md-about-gguf start --> |
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### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description) |
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GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. |
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Here is an incomplete list of clients and libraries that are known to support GGUF: |
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* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. |
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. |
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* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. |
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* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. |
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* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. |
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* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. |
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* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. |
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* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. |
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* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. |
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* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. |
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<!-- README_GGUF.md-about-gguf end --> |
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================================ |
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# #--# Original Model Card #--# |
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================================ |
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# Dolphin 2.9 Llama 3 8b 1m 🐬 |
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Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations |
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Discord: https://discord.gg/8fbBeC7ZGx |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> |
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This version of Dolphin has a 1 million token context. I have applied `winglian/llama-3-1m-context-gradient-lora` - created by @gradientai and @winglian and sponsored by @CrusoeCloud |
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A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it." |
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My appreciation for the sponsors of Dolphin 2.9: |
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node |
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This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE) |
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The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length. |
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It took 2.5 days on 8x L40S provided by Crusoe Cloud |
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This model was trained FFT on all parameters, using ChatML prompt template format. |
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example: |
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``` |
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<|im_start|>system |
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You are Dolphin, a helpful AI assistant.<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
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Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. |
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Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. |
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Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models. |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: meta-llama/Meta-Llama-3-8B |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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tokenizer_use_fast: false |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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model_config: |
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datasets: |
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- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl |
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type: sharegpt |
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conversation: chatml |
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- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl |
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type: sharegpt |
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conversation: chatml |
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chat_template: chatml |
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dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy |
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val_set_size: 0.0002 |
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output_dir: ./out |
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sequence_len: 4096 |
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sample_packing: true |
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pad_to_sequence_len: true |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 3 |
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num_epochs: 3 |
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logging_steps: 1 |
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optimizer: adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 2e-5 |
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wandb_project: dolphin-2.9-mixtral-8x22b |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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saves_per_epoch: 4 |
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save_total_limit: 2 |
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save_steps: |
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evals_per_epoch: 4 |
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eval_sample_packing: false |
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debug: |
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deepspeed: deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.05 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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eos_token: "<|im_end|>" |
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pad_token: "<|end_of_text|>" |
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tokens: |
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- "<|im_start|>" |
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- "<|im_end|>" |
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``` |
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</details><br> |
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## Quants |
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GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF |
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GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF |
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Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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- total_eval_batch_size: 24 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 7 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.146 | 0.0005 | 1 | 1.1064 | |
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| 0.6962 | 0.2501 | 555 | 0.6636 | |
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| 0.6857 | 0.5001 | 1110 | 0.6503 | |
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| 0.6592 | 0.7502 | 1665 | 0.6419 | |
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| 0.6465 | 1.0002 | 2220 | 0.6317 | |
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| 0.5295 | 1.2395 | 2775 | 0.6408 | |
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| 0.5302 | 1.4895 | 3330 | 0.6351 | |
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| 0.5188 | 1.7396 | 3885 | 0.6227 | |
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| 0.521 | 1.9896 | 4440 | 0.6168 | |
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| 0.3968 | 2.2289 | 4995 | 0.6646 | |
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| 0.3776 | 2.4789 | 5550 | 0.6619 | |
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| 0.3983 | 2.7290 | 6105 | 0.6602 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
