Instructions to use Jolly-Q/Reflection-Llama-3.1-70B_BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jolly-Q/Reflection-Llama-3.1-70B_BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Jolly-Q/Reflection-Llama-3.1-70B_BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jolly-Q/Reflection-Llama-3.1-70B_BF16")
model = AutoModelForCausalLM.from_pretrained("Jolly-Q/Reflection-Llama-3.1-70B_BF16")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jolly-Q/Reflection-Llama-3.1-70B_BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jolly-Q/Reflection-Llama-3.1-70B_BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jolly-Q/Reflection-Llama-3.1-70B_BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jolly-Q/Reflection-Llama-3.1-70B_BF16

SGLang

How to use Jolly-Q/Reflection-Llama-3.1-70B_BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Jolly-Q/Reflection-Llama-3.1-70B_BF16" \
    --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": "Jolly-Q/Reflection-Llama-3.1-70B_BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

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 "Jolly-Q/Reflection-Llama-3.1-70B_BF16" \
        --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": "Jolly-Q/Reflection-Llama-3.1-70B_BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jolly-Q/Reflection-Llama-3.1-70B_BF16 with Docker Model Runner:
```
docker model run hf.co/Jolly-Q/Reflection-Llama-3.1-70B_BF16
```

Reflection-Llama-3.1-70B_BF16 / model-00008-of-00015.safetensors

schonsense

Add files using upload-large-folder tool

0381a5f verified 4 months ago

Download

History Blame Contribute Delete

9.78 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 2f37438c436e533d85e2a62ab8d75552267db10e292ce60838e64d877c475ee8
Size of remote file:: 9.78 GB
SHA256:: b0108373dc3cbe37af7439e6b05bcb639be1dd09e57ea4138ac4efc0856144a8

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.