Comments (11)
Hello, you can find this 13B one here: https://huggingface.co/samwit/alpaca13B-lora
Otherwise, there is the 7B one here: https://huggingface.co/tloen/alpaca-lora-7b
Please note these are LoRA models they need the base model to work.
And here is the base model for the 7B: https://huggingface.co/decapoda-research/llama-7b-hf
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Any links for models trained w/3-epochs on the new cleaned dataset?
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Any links for models trained w/3-epochs on the new cleaned dataset?
I just finished training this 13B one but haven't got it to work yet (I'm using multiple GPUs so maybe that's the issue) https://huggingface.co/mattreid/alpaca-lora-13b
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@collant can you help me understand how can I load the Lora model trained with the 52k dataset and use it to train on another data.json?
In finetune.py I can find the loading of the llama 7b model
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map=device_map,
)
tokenizer = LlamaTokenizer.from_pretrained(
"decapoda-research/llama-7b-hf", add_eos_token=True
)
and after the lora config obj is being created
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=TARGET_MODULES,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
does loading the Lora model from hf involves calling another function and loading that checkpoint? I can see that there is a save_pretrained
function, maybe I need to load the Lora model via this? Sorry if this sounds confusing
edit: after a little bit more google I found this load_attn_procs function, maybe it's something around here
edit2: it seems that it was inside generate.py all along
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model, "tloen/alpaca-lora-7b",
torch_dtype=torch.float16
)
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Hello, you can find this 13B one here: https://huggingface.co/samwit/alpaca13B-lora
Otherwise, there is the 7B one here: https://huggingface.co/tloen/alpaca-lora-7b
Please note these are LoRA models they need the base model to work.
And here is the base model for the 7B: https://huggingface.co/decapoda-research/llama-7b-hf
can the original LLaMA-7B weights (consolidated.00.pth) be used? or can I convert it to hf?
from alpaca-lora.
Thank you
Hello, you can find this 13B one here: https://huggingface.co/samwit/alpaca13B-lora
Otherwise, there is the 7B one here: https://huggingface.co/tloen/alpaca-lora-7b
Please note these are LoRA models they need the base model to work.
And here is the base model for the 7B: https://huggingface.co/decapoda-research/llama-7b-hf
Thank you
from alpaca-lora.
Is there a 30B-4bit lora out there? I think I read somewhere that finetuning in 4bit might not be supported?
from alpaca-lora.
30B LoRa adapters here https://huggingface.co/baseten/alpaca-30b
from alpaca-lora.
@collant can you help me understand how can I load the Lora model trained with the 52k dataset and use it to train on another data.json?
In finetune.py I can find the loading of the llama 7b model
model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, device_map=device_map, ) tokenizer = LlamaTokenizer.from_pretrained( "decapoda-research/llama-7b-hf", add_eos_token=True )and after the lora config obj is being created
config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config)does loading the Lora model from hf involves calling another function and loading that checkpoint? I can see that there is a
save_pretrained
function, maybe I need to load the Lora model via this? Sorry if this sounds confusingedit: after a little bit more google I found this load_attn_procs function, maybe it's something around here
edit2: it seems that it was inside generate.py all along
model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained( model, "tloen/alpaca-lora-7b", torch_dtype=torch.float16 )
Have you found solution? #44 I found this may help? But I still confuse with what is
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Any links for models trained w/3-epochs on the new cleaned dataset?
+1
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Please, report @larasatistevany for spamming.
https://support.github.com/contact/report-abuse?category=report-abuse&report=larasatistevany
-> I want to report abusive content or behavior.
-> I want to report SPAM, a user that is disrupting me or my organization's experience on GitHub, or a user who is using my personal information without my permission
-> A user is disrupting me or my organization's experience and productivity by posting SPAM off-topic or other types of disruptive content in projects they do not own.
Put this in the form:
spamming in issue comments
https://github.com/tloen/alpaca-lora/issues/52#issuecomment-1570561693
https://github.com/tloen/alpaca-lora/issues/52#issuecomment-1571059071
Thanks!
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Related Issues (20)
- generate error HOT 1
- can't load tokenizer HOT 2
- Load_in_8bit causing issues: Out of memory error with 44Gb VRAM in my GPU or device_map error HOT 1
- AttributeError: module 'gradio' has no attribute 'inputs' HOT 18
- When I set load_in_8bit=true, some errors occurred....
- is there any flag to mark the model is safetensors or pickle format?
- Errors of tuning on 70B LLAMA 2, does alpaca-lora support 70B llama 2 tuning work?
- safetensors_rust.SafetensorError: Error while deserializing header: InvalidHeaderDeserialization HOT 15
- generate error after hit submit btn
- The weights are not updated HOT 1
- LAION Open Assistant data is already released
- Loading a quantized checkpoint into non-quantized Linear8bitLt is not supported
- Is it possible to combine alpaca-lora with RAG
- Is there a way to check if this training is all done?
- failed to run on colab: ModulesToSaveWrapper has no attribute `embed_tokens`
- Finetune scenarios
- decapoda-research/llama-7b-hf is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' HOT 2
- Single GPU vs multiple GPUs stack (parallel)
- Why this error? ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded: base_model.model.model.layers.3, base_model.model.model.layers.4, base_model.model.model.layers.5, base_model.model.model.layers.6, base_model.model.model.layers.7, base_model.model.model.layers.8, base_model.model.model.layers.9, base_model.model.model.layers.10, base_model.model.model.la
- InvalidHeaderDeserialization
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