Instructions to use openaccess-ai-collective/manticore-13b-chat-pyg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openaccess-ai-collective/manticore-13b-chat-pyg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openaccess-ai-collective/manticore-13b-chat-pyg")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("openaccess-ai-collective/manticore-13b-chat-pyg") model = AutoModelForMultimodalLM.from_pretrained("openaccess-ai-collective/manticore-13b-chat-pyg") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openaccess-ai-collective/manticore-13b-chat-pyg with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openaccess-ai-collective/manticore-13b-chat-pyg" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openaccess-ai-collective/manticore-13b-chat-pyg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openaccess-ai-collective/manticore-13b-chat-pyg
- SGLang
How to use openaccess-ai-collective/manticore-13b-chat-pyg with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "openaccess-ai-collective/manticore-13b-chat-pyg" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openaccess-ai-collective/manticore-13b-chat-pyg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "openaccess-ai-collective/manticore-13b-chat-pyg" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openaccess-ai-collective/manticore-13b-chat-pyg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openaccess-ai-collective/manticore-13b-chat-pyg with Docker Model Runner:
docker model run hf.co/openaccess-ai-collective/manticore-13b-chat-pyg
add axolotl config
Browse files- configs/manticore-13b-v2.yml +114 -0
configs/manticore-13b-v2.yml
ADDED
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| 1 |
+
base_model: huggyllama/llama-13b
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| 2 |
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base_model_config: huggyllama/llama-13b
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| 3 |
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model_type: LlamaForCausalLM
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| 4 |
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tokenizer_type: LlamaTokenizer
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| 5 |
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load_in_8bit: false
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| 6 |
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strict: false
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| 7 |
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push_dataset_to_hub: winglian
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| 8 |
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dataset_shard_num: 4
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| 9 |
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dataset_shard_idx: 0
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| 10 |
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datasets:
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| 11 |
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- path: redacted
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| 12 |
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data_files:
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| 13 |
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- v12_no_ai.shard_0.jsonl
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| 14 |
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type: pygmalion
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| 15 |
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- path: winglian/evals
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| 16 |
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data_files:
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| 17 |
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- hf/ARC-Challenge.jsonl
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| 18 |
+
- hf/ARC-Easy.jsonl
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| 19 |
+
- hf/riddle_sense.jsonl
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| 20 |
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type: explainchoice:chat
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| 21 |
+
- path: winglian/evals
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| 22 |
+
data_files:
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| 23 |
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- openai/tldr.jsonl
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| 24 |
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type: summarizetldr:chat
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| 25 |
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- path: winglian/evals
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| 26 |
+
data_files:
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| 27 |
+
- hf/gsm8k.jsonl
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| 28 |
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type: alpacachat.load_qa
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| 29 |
+
- path: winglian/evals
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| 30 |
+
data_files:
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| 31 |
+
- hellaswag/hellaswag.jsonl
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| 32 |
+
type: explainchoice:chat
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| 33 |
+
- path: metaeval/ScienceQA_text_only
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| 34 |
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type: concisechoice:chat
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| 35 |
+
- path: ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
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| 36 |
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type: alpaca:chat
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| 37 |
+
- path: ehartford/wizard_vicuna_70k_unfiltered
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| 38 |
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type: sharegpt:chat
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| 39 |
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- path: winglian/chatlogs-en-cleaned
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| 40 |
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data_files:
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| 41 |
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- sharegpt_cleaned.jsonl
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| 42 |
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type: sharegpt:chat
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| 43 |
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- path: teknium/GPT4-LLM-Cleaned
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| 44 |
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type: alpaca:chat
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| 45 |
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- path: teknium/GPTeacher-General-Instruct
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| 46 |
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data_files: gpt4-instruct-similarity-0.6-dataset.json
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| 47 |
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type: gpteacher:chat
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| 48 |
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- path: ewof/code-alpaca-instruct-unfiltered
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| 49 |
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type: alpaca:chat
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| 50 |
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- path: QingyiSi/Alpaca-CoT
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| 51 |
+
data_files:
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| 52 |
+
- Chain-of-Thought/formatted_cot_data/aqua_train.json [4/1757]
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| 53 |
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- Chain-of-Thought/formatted_cot_data/creak_train.json
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| 54 |
+
- Chain-of-Thought/formatted_cot_data/ecqa_train.json
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| 55 |
+
- Chain-of-Thought/formatted_cot_data/esnli_train.json
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| 56 |
+
- Chain-of-Thought/formatted_cot_data/gsm8k_train.json
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| 57 |
+
- Chain-of-Thought/formatted_cot_data/qasc_train.json
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| 58 |
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- Chain-of-Thought/formatted_cot_data/qed_train.json
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| 59 |
+
- Chain-of-Thought/formatted_cot_data/sensemaking_train.json
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| 60 |
+
- Chain-of-Thought/formatted_cot_data/strategyqa_train.json
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| 61 |
+
- GPTeacher/Roleplay/formatted_roleplay-similarity_0.6-instruct-dataset.json
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| 62 |
+
type: alpaca:chat
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| 63 |
+
dataset_prepared_path: last_run_prepared
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| 64 |
+
val_set_size: 0.02
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| 65 |
+
adapter:
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| 66 |
+
lora_model_dir:
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| 67 |
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sequence_len: 2048
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| 68 |
+
max_packed_sequence_len: 2048
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| 69 |
+
lora_r:
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| 70 |
+
lora_alpha:
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| 71 |
+
lora_dropout:
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| 72 |
+
lora_target_modules:
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| 73 |
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lora_fan_in_fan_out:
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| 74 |
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wandb_project: manticore-13b-v2
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| 75 |
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wandb_watch:
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| 76 |
+
wandb_run_id:
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| 77 |
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wandb_log_model:
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| 78 |
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output_dir: ./manticore-13b-v2
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| 79 |
+
batch_size: 512
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| 80 |
+
micro_batch_size: 8
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| 81 |
+
num_epochs: 4
|
| 82 |
+
optimizer:
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| 83 |
+
torchdistx_path:
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| 84 |
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lr_scheduler:
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| 85 |
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learning_rate: 0.00004
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| 86 |
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train_on_inputs: false
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| 87 |
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group_by_length: false
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| 88 |
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bf16: true
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| 89 |
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tf32: true
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| 90 |
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gradient_checkpointing: true
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| 91 |
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early_stopping_patience:
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| 92 |
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resume_from_checkpoint:
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| 93 |
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local_rank:
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| 94 |
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logging_steps: 1
|
| 95 |
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xformers_attention: true
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| 96 |
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flash_attention:
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| 97 |
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gptq_groupsize:
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| 98 |
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gptq_model_v1:
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| 99 |
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warmup_steps: 20
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| 100 |
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eval_steps: 10
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| 101 |
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save_steps:
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| 102 |
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debug:
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| 103 |
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deepspeed:
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| 104 |
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weight_decay: 0
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| 105 |
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fsdp:
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| 106 |
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- full_shard
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| 107 |
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- auto_wrap
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| 108 |
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fsdp_config:
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| 109 |
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
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| 110 |
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special_tokens:
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| 111 |
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bos_token: "<s>"
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| 112 |
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eos_token: "</s>"
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| 113 |
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unk_token: "<unk>"
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| 114 |
+
|