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Model: dphn/dolphin-2.9-llama3-8b-1m Source: Original Platform
2026-06-23 11:06:13 +08:00
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--- a/README.md
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+---
+license: other
+base_model: meta-llama/Meta-Llama-3-8B
+tags:
+- generated_from_trainer
+- axolotl
+model-index:
+- name: out
+  results: []
+datasets:
+- cognitivecomputations/Dolphin-2.9
+- teknium/OpenHermes-2.5
+- m-a-p/CodeFeedback-Filtered-Instruction
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- HuggingFaceH4/ultrachat_200k
+- microsoft/orca-math-word-problems-200k
+- abacusai/SystemChat-1.1
+- Locutusque/function-calling-chatml
+- internlm/Agent-FLAN
+---
+
+# Dolphin 2.9 Llama 3 8b 1m 🐬
+
+Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
+
+[![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations)
+Discord: https://discord.gg/cognitivecomputations
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
+
+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
+
+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."
+
+My appreciation for the sponsors of Dolphin 2.9:
+- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
+
+This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
+
+The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
+
+It took 2.5 days on 8x L40S provided by Crusoe Cloud
+
+This model was trained FFT on all parameters, using ChatML prompt template format.
+
+example:
+
+```
+<|im_start|>system
+You are Dolphin, a helpful AI assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+
+```
+
+Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
+
+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.
+
+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.
+
+[<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)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+base_model: meta-llama/Meta-Llama-3-8B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+tokenizer_use_fast: false
+
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+model_config:
+
+datasets:
+  - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
+    type: sharegpt  
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
+    type: sharegpt  
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
+    type: sharegpt 
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
+    type: sharegpt
+    conversation: chatml
+
+chat_template: chatml
+    
+
+dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
+val_set_size: 0.0002
+output_dir: ./out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+gradient_accumulation_steps: 4
+micro_batch_size: 3
+num_epochs: 3
+logging_steps: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+wandb_project: dolphin-2.9-mixtral-8x22b
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+saves_per_epoch: 4
+save_total_limit: 2
+save_steps:
+evals_per_epoch: 4
+eval_sample_packing: false
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.05
+fsdp:
+fsdp_config:
+special_tokens:
+  eos_token: "<|im_end|>"
+  pad_token: "<|end_of_text|>"
+tokens:
+  - "<|im_start|>"
+  - "<|im_end|>"
+
+```
+
+</details><br>
+
+## Quants
+
+GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF
+
+GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF
+
+Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 3
+- eval_batch_size: 3
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 96
+- total_eval_batch_size: 24
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 7
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.146         | 0.0005 | 1    | 1.1064          |
+| 0.6962        | 0.2501 | 555  | 0.6636          |
+| 0.6857        | 0.5001 | 1110 | 0.6503          |
+| 0.6592        | 0.7502 | 1665 | 0.6419          |
+| 0.6465        | 1.0002 | 2220 | 0.6317          |
+| 0.5295        | 1.2395 | 2775 | 0.6408          |
+| 0.5302        | 1.4895 | 3330 | 0.6351          |
+| 0.5188        | 1.7396 | 3885 | 0.6227          |
+| 0.521         | 1.9896 | 4440 | 0.6168          |
+| 0.3968        | 2.2289 | 4995 | 0.6646          |
+| 0.3776        | 2.4789 | 5550 | 0.6619          |
+| 0.3983        | 2.7290 | 6105 | 0.6602          |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.19.1
--- a/config.json
+++ b/config.json
@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "cognitivecomputations/dolphin-2.9-llama3-8b-1m",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128256,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 1048576,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 2804339835.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "vocab_size": 128258
+}
--- a/configuration.json
+++ b/configuration.json
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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
--- a/generation_config.json
+++ b/generation_config.json
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+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "transformers_version": "4.40.1"
+}
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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