trentmkelly/gpt-4o-distil
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How to use Lighstromo/Nova-Qwen-14B-LoRA with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base_model, "Lighstromo/Nova-Qwen-14B-LoRA")How to use Lighstromo/Nova-Qwen-14B-LoRA with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Lighstromo/Nova-Qwen-14B-LoRA")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lighstromo/Nova-Qwen-14B-LoRA", device_map="auto")How to use Lighstromo/Nova-Qwen-14B-LoRA with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lighstromo/Nova-Qwen-14B-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Lighstromo/Nova-Qwen-14B-LoRA",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Lighstromo/Nova-Qwen-14B-LoRA
How to use Lighstromo/Nova-Qwen-14B-LoRA with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Lighstromo/Nova-Qwen-14B-LoRA" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Lighstromo/Nova-Qwen-14B-LoRA",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "Lighstromo/Nova-Qwen-14B-LoRA" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Lighstromo/Nova-Qwen-14B-LoRA",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Lighstromo/Nova-Qwen-14B-LoRA with Docker Model Runner:
docker model run hf.co/Lighstromo/Nova-Qwen-14B-LoRA
LoRA adapter for Qwen2.5-14B-Instruct, fine-tuned on GPT-4o conversations to create a warm, creative, and intelligent conversational model.
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base_model, "Lighstromo/Nova-Qwen-14B-LoRA")
tokenizer = AutoTokenizer.from_pretrained("Lighstromo/Nova-Qwen-14B-LoRA")
Setting Value
Base Model Qwen/Qwen2.5-14B-Instruct
LoRA Rank 64
LoRA Alpha 128
Target Modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Dataset 3,066 unique GPT-4o conversations
Epochs 3
Learning Rate 2e-4 (cosine)
Final Loss 0.29
Hardware RTX 6000 Ada on RunPod
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "Lighstromo/Nova-Qwen-14B-LoRA")