--- title: dolphin-2.9.1-yi-1.5-34b canonical_url: "https://www.modelscope.cn/models/dphn/dolphin-2.9.1-yi-1.5-34b" md_url: "https://www.modelscope.cn/models/dphn/dolphin-2.9.1-yi-1.5-34b.md" repository: dphn/dolphin-2.9.1-yi-1.5-34b last_updated: 2025-09-08 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - llama architectures: - LlamaForCausalLM base_model: - 01-ai/Yi-1.5-34B base_model_relation: finetune parameters: 34.4B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 375 stars: 0 tags: - generated_from_trainer - axolotl --- # dolphin-2.9.1-yi-1.5-34b > dolphin-2.9.1-yi-1.5-34b - dphn 在 ModelScope 开源的模型。Dolphin 2.9.1 Yi 1.5 34b 🐬 dphn/dolphin-2.9.1-yi-1.5-34b 是 ModelScope 魔搭社区上的 34.4B 参数text-generation模型，采用 apache-2.0 许可，基于 01-ai/Yi-1.5-34B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: dphn/dolphin-2.9.1-yi-1.5-34b - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 34.4B - **Base model**: 01-ai/Yi-1.5-34B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: generated_from_trainer, axolotl - **Downloads**: 375 - **Stars**: 0 - **Last updated**: 2025-09-08 Source: https://www.modelscope.cn/models/dphn/dolphin-2.9.1-yi-1.5-34b --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Website: https://dphn.ai Twitter: https://x.com/dphnAI Web Chat: https://chat.dphn.ai Telegram bot: https://t.me/DolphinAI_bot Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses 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-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We 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 apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.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 chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<|startoftext|>" eos_token: "<|im_end|>" pad_token: "" unk_token: "" tokens: - "<|im_start|>" ```

# out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6265 | 0.0 | 1 | 0.6035 | | 0.4674 | 0.25 | 327 | 0.4344 | | 0.4337 | 0.5 | 654 | 0.4250 | | 0.4346 | 0.75 | 981 | 0.4179 | | 0.3985 | 1.0 | 1308 | 0.4118 | | 0.3128 | 1.23 | 1635 | 0.4201 | | 0.3261 | 1.48 | 1962 | 0.4157 | | 0.3259 | 1.73 | 2289 | 0.4122 | | 0.3126 | 1.98 | 2616 | 0.4079 | | 0.2265 | 2.21 | 2943 | 0.4441 | | 0.2297 | 2.46 | 3270 | 0.4427 | | 0.2424 | 2.71 | 3597 | 0.4425 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0