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dolphin-2.9.1-qwen-110b

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
axolotl
conversational
Inference Endpoints
text-generation-inference
Model card Files Community
1
dolphin-2.9.1-qwen-110b
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---
license: other
license_name: tongyi-qianwen
license_link: >-
  https://huggingface.co/Qwen/Qwen1.5-110B/blob/main/LICENSE
base_model: Qwen/Qwen1.5-110B
tags:
- generated_from_trainer
- axolotl
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
---

# Dolphin 2.9.1 Qwen 110b 🐬

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" />

Our appreciation for the sponsors of Dolphin 2.9.1:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node

This model is based on Qwen1.5-110B, and is governed by [tongyi-qianwen license](LICENSE)

The base model has 32k context, and the full-weight fine-tuning was with 8k sequence length.

This model was trained FFT on parameters selected by [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py), 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-2.9.1 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 Qwen's tongyi-qianwen license.  We grant permission for any use, including commercial, that falls within accordance with said license. Dolphin was trained on data generated from GPT4, among other models.

## Evals

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/U86Zu-MzLq4rECJRAAvgq.png)

## Training

[<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: /workspace/axolotl/qwen-checkpoint
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# trust_remote_code: true

# load_in_8bit: true
# load_in_4bit: true
# strict: false

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: last_run_prepared
val_set_size: 0.01
output_dir: ./qwen-out

# adapter: qlora
# lora_r: 16
# lora_alpha: 16
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_dropout: 0.05
# lora_target_linear: false

unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
# input_layernorm layers
- model.layers.0.input_layernorm
- model.layers.1.input_layernorm
- model.layers.2.input_layernorm
- model.layers.3.input_layernorm
- model.layers.4.input_layernorm
- model.layers.5.input_layernorm
- model.layers.6.input_layernorm
- model.layers.7.input_layernorm
- model.layers.8.input_layernorm
- model.layers.9.input_layernorm
- model.layers.10.input_layernorm
- model.layers.11.input_layernorm
- model.layers.12.input_layernorm
- model.layers.13.input_layernorm
- model.layers.14.input_layernorm
- model.layers.15.input_layernorm
- model.layers.16.input_layernorm
- model.layers.17.input_layernorm
- model.layers.18.input_layernorm
- model.layers.19.input_layernorm
- model.layers.20.input_layernorm
- model.layers.21.input_layernorm
- model.layers.22.input_layernorm
- model.layers.23.input_layernorm
# lm_head layers
# mlp.down_proj layers
- model.layers.17.mlp.down_proj
- model.layers.18.mlp.down_proj
- model.layers.19.mlp.down_proj
- model.layers.20.mlp.down_proj
- model.layers.21.mlp.down_proj
- model.layers.22.mlp.down_proj
- model.layers.23.mlp.down_proj
- model.layers.24.mlp.down_proj
- model.layers.25.mlp.down_proj
- model.layers.26.mlp.down_proj
- model.layers.27.mlp.down_proj
- model.layers.28.mlp.down_proj
- model.layers.29.mlp.down_proj
- model.layers.30.mlp.down_proj
- model.layers.31.mlp.down_proj
- model.layers.32.mlp.down_proj
- model.layers.33.mlp.down_proj
- model.layers.34.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.36.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.39.mlp.down_proj
- model.layers.40.mlp.down_proj
# mlp.gate_proj layers
- model.layers.51.mlp.gate_proj
- model.layers.50.mlp.gate_proj
- model.layers.53.mlp.gate_proj
- model.layers.52.mlp.gate_proj
- model.layers.49.mlp.gate_proj
- model.layers.45.mlp.gate_proj
- model.layers.46.mlp.gate_proj
- model.layers.47.mlp.gate_proj
- model.layers.57.mlp.gate_proj
- model.layers.48.mlp.gate_proj
- model.layers.56.mlp.gate_proj
- model.layers.41.mlp.gate_proj
- model.layers.54.mlp.gate_proj
- model.layers.43.mlp.gate_proj
- model.layers.44.mlp.gate_proj
- model.layers.60.mlp.gate_proj
- model.layers.55.mlp.gate_proj
- model.layers.40.mlp.gate_proj
- model.layers.42.mlp.gate_proj
- model.layers.58.mlp.gate_proj
- model.layers.36.mlp.gate_proj
- model.layers.37.mlp.gate_proj
- model.layers.38.mlp.gate_proj
- model.layers.39.mlp.gate_proj
# mlp.up_proj layers
- model.layers.50.mlp.up_proj
- model.layers.51.mlp.up_proj
- model.layers.41.mlp.up_proj
- model.layers.49.mlp.up_proj
- model.layers.43.mlp.up_proj
- model.layers.44.mlp.up_proj
- model.layers.40.mlp.up_proj
- model.layers.45.mlp.up_proj
- model.layers.47.mlp.up_proj
- model.layers.48.mlp.up_proj
- model.layers.46.mlp.up_proj
- model.layers.42.mlp.up_proj
- model.layers.39.mlp.up_proj
- model.layers.36.mlp.up_proj
- model.layers.37.mlp.up_proj
- model.layers.38.mlp.up_proj
- model.layers.56.mlp.up_proj
- model.layers.57.mlp.up_proj
- model.layers.53.mlp.up_proj
- model.layers.31.mlp.up_proj
- model.layers.32.mlp.up_proj
- model.layers.34.mlp.up_proj
- model.layers.35.mlp.up_proj
- model.layers.33.mlp.up_proj
# model.embed_tokens layers
# model.norm layers
# post_attention_layernorm layers
- model.layers.0.post_attention_layernorm
- model.layers.1.post_attention_layernorm
- model.layers.2.post_attention_layernorm
- model.layers.3.post_attention_layernorm
- model.layers.4.post_attention_layernorm
- model.layers.5.post_attention_layernorm
- model.layers.6.post_attention_layernorm
- model.layers.7.post_attention_layernorm
- model.layers.8.post_attention_layernorm
- model.layers.9.post_attention_layernorm
- model.layers.10.post_attention_layernorm
- model.layers.11.post_attention_layernorm
- model.layers.12.post_attention_layernorm
- model.layers.13.post_attention_layernorm
- model.layers.14.post_attention_layernorm
- model.layers.15.post_attention_layernorm
- model.layers.16.post_attention_layernorm
- model.layers.17.post_attention_layernorm
- model.layers.18.post_attention_layernorm
- model.layers.19.post_attention_layernorm
- model.layers.20.post_attention_layernorm
- model.layers.21.post_attention_layernorm
- model.layers.22.post_attention_layernorm
- model.layers.23.post_attention_layernorm
# self_attn.k_proj layers
- model.layers.42.self_attn.k_proj
- model.layers.41.self_attn.k_proj
- model.layers.39.self_attn.k_proj
- model.layers.35.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.79.self_attn.k_proj
- model.layers.43.self_attn.k_proj
- model.layers.32.self_attn.k_proj
- model.layers.73.self_attn.k_proj
- model.layers.31.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.76.self_attn.k_proj
- model.layers.30.self_attn.k_proj
- model.layers.40.self_attn.k_proj
- model.layers.33.self_attn.k_proj
- model.layers.78.self_attn.k_proj
- model.layers.34.self_attn.k_proj
- model.layers.37.self_attn.k_proj
- model.layers.45.self_attn.k_proj
- model.layers.44.self_attn.k_proj
- model.layers.71.self_attn.k_proj
- model.layers.26.self_attn.k_proj
- model.layers.74.self_attn.k_proj
- model.layers.27.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.35.self_attn.o_proj
- model.layers.34.self_attn.o_proj
- model.layers.37.self_attn.o_proj
- model.layers.33.self_attn.o_proj
- model.layers.31.self_attn.o_proj
- model.layers.27.self_attn.o_proj
- model.layers.38.self_attn.o_proj
- model.layers.24.self_attn.o_proj
- model.layers.39.self_attn.o_proj
- model.layers.43.self_attn.o_proj
- model.layers.29.self_attn.o_proj
- model.layers.0.self_attn.o_proj
- model.layers.50.self_attn.o_proj
- model.layers.32.self_attn.o_proj
- model.layers.45.self_attn.o_proj
- model.layers.30.self_attn.o_proj
- model.layers.60.self_attn.o_proj
- model.layers.23.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.67.self_attn.o_proj
- model.layers.57.self_attn.o_proj
- model.layers.20.self_attn.o_proj
- model.layers.76.self_attn.o_proj
- model.layers.28.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.1.self_attn.q_proj
- model.layers.6.self_attn.q_proj
- model.layers.0.self_attn.q_proj
- model.layers.5.self_attn.q_proj
- model.layers.2.self_attn.q_proj
- model.layers.7.self_attn.q_proj
- model.layers.3.self_attn.q_proj
- model.layers.4.self_attn.q_proj
- model.layers.8.self_attn.q_proj
- model.layers.9.self_attn.q_proj
- model.layers.61.self_attn.q_proj
- model.layers.10.self_attn.q_proj
- model.layers.62.self_attn.q_proj
- model.layers.36.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.17.self_attn.q_proj
- model.layers.60.self_attn.q_proj
- model.layers.63.self_attn.q_proj
- model.layers.64.self_attn.q_proj
- model.layers.29.self_attn.q_proj
- model.layers.30.self_attn.q_proj
- model.layers.55.self_attn.q_proj
- model.layers.34.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.12.self_attn.v_proj
- model.layers.16.self_attn.v_proj
- model.layers.18.self_attn.v_proj
- model.layers.19.self_attn.v_proj
- model.layers.20.self_attn.v_proj
- model.layers.21.self_attn.v_proj
- model.layers.22.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.24.self_attn.v_proj
- model.layers.25.self_attn.v_proj
- model.layers.26.self_attn.v_proj
- model.layers.27.self_attn.v_proj
- model.layers.28.self_attn.v_proj
- model.layers.29.self_attn.v_proj
- model.layers.30.self_attn.v_proj
- model.layers.31.self_attn.v_proj
- model.layers.32.self_attn.v_proj
- model.layers.33.self_attn.v_proj
- model.layers.34.self_attn.v_proj
- model.layers.35.self_attn.v_proj
- model.layers.36.self_attn.v_proj
- model.layers.37.self_attn.v_proj
- model.layers.38.self_attn.v_proj
- model.layers.39.self_attn.v_proj



sequence_len: 8192 # supports up to 8192
sample_packing: true
pad_to_sequence_len: true

# adapter: lora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# lora_fan_in_fan_out:

wandb_project: dolphin-2.9-qwen-1.5-110b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
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
early_stopping_patience:
# resume_from_checkpoint: /workspace/axolotl/qwen-checkpoint
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 4
save_total_limit: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"



```

</details><br>

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3528        | 0.0   | 1    | 0.3848          |
| 0.3687        | 0.25  | 291  | 0.3988          |
| 0.4156        | 0.5   | 582  | 0.3966          |
| 0.3826        | 0.75  | 873  | 0.3931          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0