This is a LoRA fine-tuned version of the Jamba model trained on chat conversations.
Model Description
Base Model:
LaferriereJC/jamba_550M_trained
Training Data:
UltraChat dataset
Task:
Conversational AI
Fine-tuning Method:
LoRA (Low-Rank Adaptation)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
# Load the model
model = AutoModelForCausalLM.from_pretrained(
"LaferriereJC/jamba_550M_trained",
trust_remote_code=True
)
model = PeftModel.from_pretrained(model, "your-username/jamba-chat-lora")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("LaferriereJC/jamba_550M_trained")
# Example usage
text = "User: How are you today?\nAssistant:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Training Details
Training Data:
UltraChat dataset (subset)
LoRA Config:
Rank: 16
Alpha: 32
Target Modules: Last layer feed forward experts
Dropout: 0.1
Training Parameters:
Learning Rate: 5e-4
Optimizer: AdamW (32-bit)
LR Scheduler: Cosine
Warmup Ratio: 0.03
Runs of Joshfcooper jamba-chat-lora on huggingface.co
0
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
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30-day runs
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