argilla/OpenHermes2.5-dpo-binarized-alpha
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How to use frankenmerger/MiniLlama-1.8b-Chat-v0.1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="frankenmerger/MiniLlama-1.8b-Chat-v0.1")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("frankenmerger/MiniLlama-1.8b-Chat-v0.1")
model = AutoModelForCausalLM.from_pretrained("frankenmerger/MiniLlama-1.8b-Chat-v0.1")
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 frankenmerger/MiniLlama-1.8b-Chat-v0.1 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frankenmerger/MiniLlama-1.8b-Chat-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "frankenmerger/MiniLlama-1.8b-Chat-v0.1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/frankenmerger/MiniLlama-1.8b-Chat-v0.1
How to use frankenmerger/MiniLlama-1.8b-Chat-v0.1 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "frankenmerger/MiniLlama-1.8b-Chat-v0.1" \
--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": "frankenmerger/MiniLlama-1.8b-Chat-v0.1",
"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 "frankenmerger/MiniLlama-1.8b-Chat-v0.1" \
--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": "frankenmerger/MiniLlama-1.8b-Chat-v0.1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use frankenmerger/MiniLlama-1.8b-Chat-v0.1 with Docker Model Runner:
docker model run hf.co/frankenmerger/MiniLlama-1.8b-Chat-v0.1
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "frankenmerger/MiniLlama-1.8b-Chat-v0.1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0