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Model: dphn/dolphin-2.8-mistral-7b-v02 Source: Original Platform
2026-05-07 10:45:34 +08:00
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+---
+base_model: alpindale/Mistral-7B-v0.2-hf
+language:
+- en
+license: apache-2.0
+datasets:
+- cognitivecomputations/dolphin
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- jondurbin/airoboros-2.2.1
+- teknium/openhermes-2.5
+- m-a-p/Code-Feedback
+- m-a-p/CodeFeedback-Filtered-Instruction
+model-index:
+- name: dolphin-2.8-mistral-7b-v02
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.469
+      verified: false
+---
+
+# Dolphin 2.8 Mistral 7b v0.2 🐬
+
+By Eric Hartford 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" />
+
+My appreciation for the sponsors of Dolphin 2.8:
+- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
+- [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource!
+- [Abacus AI](https://abacus.ai/) - my employer and partner in many things.
+
+This model is based on [Mistral-7b-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace.  Thanks to @alpindale for converting / publishing.
+
+The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths.
+
+It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/)
+
+Dolphin-2.8 has a variety of instruction, conversational, and coding skills.
+
+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 to 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 Apache 2.0.  I grant permission for any use including commercial.  Dolphin was trained on data generated from GPT4 among other models.
+
+# Evals
+
+```
+{
+  "arc_challenge": {
+    "acc,none": 0.5921501706484642,
+    "acc_stderr,none": 0.014361097288449701,
+    "acc_norm,none": 0.6339590443686007,
+    "acc_norm_stderr,none": 0.014077223108470139
+  },
+  "gsm8k": {
+    "exact_match,strict-match": 0.4783927217589083,
+    "exact_match_stderr,strict-match": 0.013759618667051773,
+    "exact_match,flexible-extract": 0.5367702805155421,
+    "exact_match_stderr,flexible-extract": 0.013735191956468648
+  },
+  "hellaswag": {
+    "acc,none": 0.6389165504879506,
+    "acc_stderr,none": 0.004793330525656218,
+    "acc_norm,none": 0.8338976299541924,
+    "acc_norm_stderr,none": 0.00371411888431746
+  },
+  "mmlu": {
+    "acc,none": 0.6122347243982339,
+    "acc_stderr,none": 0.003893774654142997
+  },
+  "truthfulqa_mc2": {
+    "acc,none": 0.5189872652778472,
+    "acc_stderr,none": 0.014901128316426086
+  },
+  "winogrande": {
+    "acc,none": 0.7971586424625099,
+    "acc_stderr,none": 0.011301439925936643
+  }
+}
+```
+
+[<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: alpindale/Mistral-7B-v0.2-hf
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: /workspace/datasets/dolphin201-sharegpt2.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/not_samantha_norefusals.jsonl
+    type: sharegpt
+  - path: /workspace/datasets/openhermes2_5-sharegpt.jsonl
+    type: sharegpt
+
+chat_template: chatml
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.001
+output_dir: /workspace/dolphin-2.8-mistral-7b
+
+sequence_len: 16384
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project: dolphin
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 3
+num_epochs: 4
+adam_beta2: 0.95
+adam_epsilon: 0.00001
+max_grad_norm: 1.0
+lr_scheduler: cosine
+learning_rate: 0.000005
+optimizer: adamw_bnb_8bit
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+
+eval_steps: 73
+eval_table_size:
+eval_table_max_new_tokens:
+eval_sample_packing: false
+saves_per_epoch: 
+save_steps: 73
+save_total_limit: 2
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  eos_token: "<|im_end|>"
+tokens:
+  - "<|im_start|>"
+
+```
+
+</details><br>
+
+# workspace/dolphin-2.8-mistral-7b
+
+This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4828
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-06
+- train_batch_size: 3
+- eval_batch_size: 3
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 10
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 240
+- total_eval_batch_size: 30
+- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.1736        | 0.0   | 1    | 1.0338          |
+| 0.6106        | 0.36  | 73   | 0.5439          |
+| 0.5766        | 0.72  | 146  | 0.5171          |
+| 0.5395        | 1.06  | 219  | 0.5045          |
+| 0.5218        | 1.42  | 292  | 0.4976          |
+| 0.5336        | 1.78  | 365  | 0.4915          |
+| 0.5018        | 2.13  | 438  | 0.4885          |
+| 0.5113        | 2.48  | 511  | 0.4856          |
+| 0.5066        | 2.84  | 584  | 0.4838          |
+| 0.4967        | 3.19  | 657  | 0.4834          |
+| 0.4956        | 3.55  | 730  | 0.4830          |
+| 0.5026        | 3.9   | 803  | 0.4828          |
+
+
+### Framework versions
+
+- Transformers 4.40.0.dev0
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.0
+
+
+# Quants
+
+- [dagbs/-GGUF](https://huggingface.co/dagbs/dolphin-2.8-mistral-7b-v02-GGUF)
+
+- [bartowski/ExLlamaV2](https://huggingface.co/bartowski/dolphin-2.8-mistral-7b-v02-exl2)
+
+- [solidrust/AWQ](https://huggingface.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ)