metadata
license: llama3
base_model: meta-llama/Meta-Llama-3-70B
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
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
Dolphin 2.9.1 Llama 3 70b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
We have retrained our LLama-3-70b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be too reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9.
Our appreciation for the sponsors of Dolphin 2.9.1:
- Crusoe Cloud - provided excellent on-demand 8xL40S node
- OnDemand - provided inference sponsorship
This model is based on Llama-3-70b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 3 days on an 8x H100 provided by Crusoe Cloud
This model was trained FFT on parameters selected by Laser Scanner, 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 Meta's Llama license. We 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.
Evals
Training
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-70B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
# 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/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
# adapter: qlora
# lora_r: 128
# lora_alpha: 16
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_dropout: 0.05
# lora_target_linear: true
unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
# mlp.down_proj layers
- model.layers.40.mlp.down_proj
- model.layers.44.mlp.down_proj
- model.layers.45.mlp.down_proj
- model.layers.46.mlp.down_proj
- model.layers.43.mlp.down_proj
- model.layers.52.mlp.down_proj
- model.layers.47.mlp.down_proj
- model.layers.48.mlp.down_proj
- model.layers.39.mlp.down_proj
- model.layers.49.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.53.mlp.down_proj
- model.layers.41.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.51.mlp.down_proj
- model.layers.42.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.50.mlp.down_proj
- model.layers.60.mlp.down_proj
- model.layers.76.mlp.down_proj
- model.layers.54.mlp.down_proj
- model.layers.36.mlp.down_proj
- model.layers.57.mlp.down_proj
- model.layers.56.mlp.down_proj
- model.layers.55.mlp.down_proj
- model.layers.77.mlp.down_proj
- model.layers.59.mlp.down_proj
- model.layers.61.mlp.down_proj
- model.layers.58.mlp.down_proj
- model.layers.65.mlp.down_proj
- model.layers.75.mlp.down_proj
- model.layers.64.mlp.down_proj
- model.layers.62.mlp.down_proj
- model.layers.68.mlp.down_proj
- model.layers.19.mlp.down_proj
- model.layers.66.mlp.down_proj
# mlp.gate_proj layers
- model.layers.70.mlp.gate_proj
- model.layers.71.mlp.gate_proj
- model.layers.67.mlp.gate_proj
- model.layers.58.mlp.gate_proj
- model.layers.55.mlp.gate_proj
- model.layers.57.mlp.gate_proj
- model.layers.56.mlp.gate_proj
- model.layers.66.mlp.gate_proj
- model.layers.72.mlp.gate_proj
- model.layers.69.mlp.gate_proj
- model.layers.52.mlp.gate_proj
- model.layers.54.mlp.gate_proj
- model.layers.62.mlp.gate_proj
- model.layers.60.mlp.gate_proj
- model.layers.74.mlp.gate_proj
- model.layers.59.mlp.gate_proj
- model.layers.68.mlp.gate_proj
- model.layers.61.mlp.gate_proj
- model.layers.73.mlp.gate_proj
- model.layers.53.mlp.gate_proj
- model.layers.51.mlp.gate_proj
- model.layers.63.mlp.gate_proj
- model.layers.48.mlp.gate_proj
- model.layers.49.mlp.gate_proj
- model.layers.64.mlp.gate_proj
- model.layers.50.mlp.gate_proj
- model.layers.65.mlp.gate_proj
- model.layers.47.mlp.gate_proj
- model.layers.44.mlp.gate_proj
- model.layers.45.mlp.gate_proj
- model.layers.75.mlp.gate_proj
- model.layers.46.mlp.gate_proj
- model.layers.43.mlp.gate_proj
- model.layers.77.mlp.gate_proj
- model.layers.41.mlp.gate_proj
- model.layers.42.mlp.gate_proj
# mlp.up_proj layers
- model.layers.70.mlp.up_proj
- model.layers.67.mlp.up_proj
- model.layers.66.mlp.up_proj
- model.layers.69.mlp.up_proj
- model.layers.62.mlp.up_proj
- model.layers.65.mlp.up_proj
- model.layers.63.mlp.up_proj
- model.layers.68.mlp.up_proj
- model.layers.71.mlp.up_proj
- model.layers.64.mlp.up_proj
- model.layers.61.mlp.up_proj
- model.layers.58.mlp.up_proj
- model.layers.59.mlp.up_proj
- model.layers.57.mlp.up_proj
- model.layers.55.mlp.up_proj
- model.layers.72.mlp.up_proj
- model.layers.54.mlp.up_proj
- model.layers.60.mlp.up_proj
- model.layers.56.mlp.up_proj
- model.layers.73.mlp.up_proj
- model.layers.50.mlp.up_proj
- model.layers.51.mlp.up_proj
- model.layers.53.mlp.up_proj
- model.layers.74.mlp.up_proj
- model.layers.52.mlp.up_proj
- model.layers.49.mlp.up_proj
- model.layers.30.mlp.up_proj
- model.layers.34.mlp.up_proj
- model.layers.47.mlp.up_proj
- model.layers.46.mlp.up_proj
- model.layers.48.mlp.up_proj
- model.layers.38.mlp.up_proj
- model.layers.45.mlp.up_proj
- model.layers.43.mlp.up_proj
- model.layers.29.mlp.up_proj
- model.layers.42.mlp.up_proj
# self_attn.k_proj layers
- model.layers.72.self_attn.k_proj
- model.layers.75.self_attn.k_proj
- model.layers.71.self_attn.k_proj
- model.layers.74.self_attn.k_proj
- model.layers.44.self_attn.k_proj
- model.layers.31.self_attn.k_proj
- model.layers.33.self_attn.k_proj
- model.layers.34.self_attn.k_proj
- model.layers.76.self_attn.k_proj
- model.layers.78.self_attn.k_proj
- model.layers.77.self_attn.k_proj
- model.layers.22.self_attn.k_proj
- model.layers.18.self_attn.k_proj
- model.layers.60.self_attn.k_proj
- model.layers.17.self_attn.k_proj
- model.layers.56.self_attn.k_proj
- model.layers.2.self_attn.k_proj
- model.layers.21.self_attn.k_proj
- model.layers.19.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.52.self_attn.k_proj
- model.layers.35.self_attn.k_proj
- model.layers.73.self_attn.k_proj
- model.layers.15.self_attn.k_proj
- model.layers.27.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.20.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.36.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.37.self_attn.k_proj
- model.layers.30.self_attn.k_proj
- model.layers.16.self_attn.k_proj
- model.layers.32.self_attn.k_proj
- model.layers.41.self_attn.k_proj
- model.layers.26.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.50.self_attn.o_proj
- model.layers.61.self_attn.o_proj
- model.layers.46.self_attn.o_proj
- model.layers.53.self_attn.o_proj
- model.layers.54.self_attn.o_proj
- model.layers.19.self_attn.o_proj
- model.layers.42.self_attn.o_proj
- model.layers.49.self_attn.o_proj
- model.layers.41.self_attn.o_proj
- model.layers.68.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.45.self_attn.o_proj
- model.layers.11.self_attn.o_proj
- model.layers.67.self_attn.o_proj
- model.layers.48.self_attn.o_proj
- model.layers.51.self_attn.o_proj
- model.layers.64.self_attn.o_proj
- model.layers.13.self_attn.o_proj
- model.layers.14.self_attn.o_proj
- model.layers.16.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.47.self_attn.o_proj
- model.layers.0.self_attn.o_proj
- model.layers.20.self_attn.o_proj
- model.layers.63.self_attn.o_proj
- model.layers.15.self_attn.o_proj
- model.layers.5.self_attn.o_proj
- model.layers.21.self_attn.o_proj
- model.layers.52.self_attn.o_proj
- model.layers.12.self_attn.o_proj
- model.layers.10.self_attn.o_proj
- model.layers.62.self_attn.o_proj
- model.layers.56.self_attn.o_proj
- model.layers.22.self_attn.o_proj
- model.layers.6.self_attn.o_proj
- model.layers.7.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.2.self_attn.q_proj
- model.layers.4.self_attn.q_proj
- model.layers.46.self_attn.q_proj
- model.layers.5.self_attn.q_proj
- model.layers.7.self_attn.q_proj
- model.layers.6.self_attn.q_proj
- model.layers.9.self_attn.q_proj
- model.layers.10.self_attn.q_proj
- model.layers.1.self_attn.q_proj
- model.layers.18.self_attn.q_proj
- model.layers.62.self_attn.q_proj
- model.layers.8.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.14.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.31.self_attn.q_proj
- model.layers.19.self_attn.q_proj
- model.layers.17.self_attn.q_proj
- model.layers.33.self_attn.q_proj
- model.layers.35.self_attn.q_proj
- model.layers.12.self_attn.q_proj
- model.layers.21.self_attn.q_proj
- model.layers.27.self_attn.q_proj
- model.layers.34.self_attn.q_proj
- model.layers.13.self_attn.q_proj
- model.layers.56.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.52.self_attn.q_proj
- model.layers.54.self_attn.q_proj
- model.layers.28.self_attn.q_proj
- model.layers.30.self_attn.q_proj
- model.layers.20.self_attn.q_proj
- model.layers.29.self_attn.q_proj
- model.layers.37.self_attn.q_proj
- model.layers.23.self_attn.q_proj
- model.layers.75.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.11.self_attn.v_proj
- model.layers.17.self_attn.v_proj
- model.layers.37.self_attn.v_proj
- model.layers.40.self_attn.v_proj
- model.layers.41.self_attn.v_proj
- model.layers.42.self_attn.v_proj
- model.layers.43.self_attn.v_proj
- model.layers.44.self_attn.v_proj
- model.layers.45.self_attn.v_proj
- model.layers.46.self_attn.v_proj
- model.layers.48.self_attn.v_proj
- model.layers.49.self_attn.v_proj
- model.layers.50.self_attn.v_proj
- model.layers.51.self_attn.v_proj
- model.layers.53.self_attn.v_proj
- model.layers.54.self_attn.v_proj
- model.layers.55.self_attn.v_proj
- model.layers.57.self_attn.v_proj
- model.layers.58.self_attn.v_proj
- model.layers.59.self_attn.v_proj
- model.layers.60.self_attn.v_proj
- model.layers.61.self_attn.v_proj
- model.layers.62.self_attn.v_proj
- model.layers.63.self_attn.v_proj
- model.layers.64.self_attn.v_proj
- model.layers.65.self_attn.v_proj
- model.layers.66.self_attn.v_proj
- model.layers.67.self_attn.v_proj
- model.layers.69.self_attn.v_proj
- model.layers.75.self_attn.v_proj
- model.layers.18.self_attn.v_proj
- model.layers.78.self_attn.v_proj
- model.layers.68.self_attn.v_proj
- model.layers.47.self_attn.v_proj
- model.layers.38.self_attn.v_proj
- model.layers.71.self_attn.v_proj
# model.norm layers
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: /workspace/axolotl/llama-70b
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
wandb_project: llama-3
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:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 5
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
save_total_limit: 2
save_steps:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.00
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
workspace/axolotl/llama-70b
This model is a fine-tuned version of meta-llama/Meta-Llama-3-70B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4808
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: 5
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7659 | 0.0004 | 1 | 0.7454 |
| 0.5006 | 0.2501 | 587 | 0.4817 |
| 0.4807 | 0.5002 | 1174 | 0.4698 |
| 0.4758 | 0.7503 | 1761 | 0.4627 |
| 0.4969 | 1.0004 | 2348 | 0.4558 |
| 0.3604 | 1.2346 | 2935 | 0.4635 |
| 0.3817 | 1.4847 | 3522 | 0.4572 |
| 0.377 | 1.7348 | 4109 | 0.4533 |
| 0.3695 | 1.9849 | 4696 | 0.4487 |
| 0.2676 | 2.2187 | 5283 | 0.4825 |
| 0.255 | 2.4688 | 5870 | 0.4814 |
| 0.2851 | 2.7189 | 6457 | 0.4808 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1