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And load the model using the load function where I pass the adapters path.
# Load the fine-tuned model with LoRA weights
model_lora, tokenizer_lora = load(
path_or_hf_repo="mlx-community/Meta-Llama-3-8B-Instruct-4bit",
adapter_path="adapters"
)
I get nicely generated outputs able to solve annoyingly verbose math problems. Like so (I called my model LlaMATH).
However when fusing using the code below and importing the fused model from either hugging face or locally the outputs are bad.
# Can also load the fused model locally
fused_model, fused_tokenizer = load("./lora_fused_model/")
# Running model with test question
response = generate(
fused_model,
fused_tokenizer,
prompt=prompt,
max_tokens=100,
temp=0.0,
verbose=False
)
This will give me some nonsense. Any help would be great. I am using python 3.11, and mlx_lm.version == 0.12.1. Thank you and I appreciate any advice or help! :D
The text was updated successfully, but these errors were encountered:
Hi,
When I train my model using the code below (Note this was all done in jupyter Notebook).
And load the model using the load function where I pass the adapters path.
I get nicely generated outputs able to solve annoyingly verbose math problems. Like so (I called my model LlaMATH).
However when fusing using the code below and importing the fused model from either hugging face or locally the outputs are bad.
Using the fused model
This will give me some nonsense. Any help would be great. I am using python 3.11, and mlx_lm.version == 0.12.1. Thank you and I appreciate any advice or help! :D
The text was updated successfully, but these errors were encountered: