hieunguyenminh/roleplay
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How to use hieunguyenminh/v3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="hieunguyenminh/v3")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("hieunguyenminh/v3")
model = AutoModelForCausalLM.from_pretrained("hieunguyenminh/v3")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use hieunguyenminh/v3 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hieunguyenminh/v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "hieunguyenminh/v3",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/hieunguyenminh/v3
How to use hieunguyenminh/v3 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "hieunguyenminh/v3" \
--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": "hieunguyenminh/v3",
"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 "hieunguyenminh/v3" \
--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": "hieunguyenminh/v3",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use hieunguyenminh/v3 with Docker Model Runner:
docker model run hf.co/hieunguyenminh/v3
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on the hieunguyenminh/roleplay dataset.
This model can adapt to any type of characters and provide answer that personalize that character.
It is trained with supervised learning and will be trained with DPO in the future.
The following hyperparameters were used during training:
Loss after 400 steps: 0.73