Instructions to use gradientai/Llama-3-8B-Instruct-262k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gradientai/Llama-3-8B-Instruct-262k with Transformers:

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

pipe = pipeline("text-generation", model="gradientai/Llama-3-8B-Instruct-262k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gradientai/Llama-3-8B-Instruct-262k")
model = AutoModelForCausalLM.from_pretrained("gradientai/Llama-3-8B-Instruct-262k")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gradientai/Llama-3-8B-Instruct-262k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gradientai/Llama-3-8B-Instruct-262k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gradientai/Llama-3-8B-Instruct-262k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gradientai/Llama-3-8B-Instruct-262k

SGLang

How to use gradientai/Llama-3-8B-Instruct-262k 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 "gradientai/Llama-3-8B-Instruct-262k" \
    --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": "gradientai/Llama-3-8B-Instruct-262k",
		"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 "gradientai/Llama-3-8B-Instruct-262k" \
        --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": "gradientai/Llama-3-8B-Instruct-262k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gradientai/Llama-3-8B-Instruct-262k with Docker Model Runner:
```
docker model run hf.co/gradientai/Llama-3-8B-Instruct-262k
```

Uploading EXL2 quants

by bullerwins - opened Apr 26, 2024

Discussion

bullerwins

Apr 26, 2024

8.0bpw exl2 https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_exl2_8.0bpw

bullerwins

Apr 26, 2024

6.0bpw exl2 https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_exl2_6.0bpw

bullerwins

Apr 26, 2024

5.0bpw exl2 https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_exl2_5.0bpw

michaelfeil

Apr 27, 2024

•

edited Apr 27, 2024

Amazing @bullerwins . Just curious:

How did you quantize them / which scripts?
which UI / Inference engine are you using for the exl2 quants and local inference?

michaelfeil

Apr 29, 2024

@bullerwins Feel free to open a PR to the Readme!

bullerwins

Apr 30, 2024

Amazing @bullerwins . Just curious:

How did you quantize them / which scripts?

which UI / Inference engine are you using for the exl2 quants and local inference?

I'm using Turboderp's exllamav2 https://github.com/turboderp/exllamav2

Script:
python3 convert.py -i gradientai_Llama-3-8B-Instruct-262k/ -o /temp/ -cf gradientai_Llama-3-8B-Instruct-262k_exl2_5.0bpw/ -b 5.0

I'm testing it using Oobabooga's Textgen webui for inference: https://github.com/oobabooga/text-generation-webui

michaelfeil

May 3, 2024

@bullerwins Working on a revised version (with better chat alignment). Would you be up for creating quants? Id be linking them in the readme again

Also: We released the better alignment for 70-B.
https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-262k

michaelfeil

May 4, 2024

@bullerwins We just upgraded the weights, you should see a drastic improvment over the previous iteration.

bullerwins

May 4, 2024

Working on the upgraded 8B weights exl2 quants as well as 70B with better alignment

bullerwins

May 4, 2024

Exl2 quants for today's (4th of May 2024) updated weights:

I called them v2 for clarity

8.0bpw https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_v2_exl2_8.0bpw
6.0bpw https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_v2_exl2_6.0bpw
5.0bpw https://huggingface.co/bullerwins/gradientai_Llama-3-8B-Instruct-262k_v2_exl2_5.0bpw

Opening a PR for the readme

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