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

Libraries

How to use johnsutor/Llama-3-8B-Instruct_linear with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("johnsutor/Llama-3-8B-Instruct_linear")
model = AutoModelForCausalLM.from_pretrained("johnsutor/Llama-3-8B-Instruct_linear")
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

vLLM

How to use johnsutor/Llama-3-8B-Instruct_linear with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/johnsutor/Llama-3-8B-Instruct_linear

SGLang

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

Docker Model Runner
How to use johnsutor/Llama-3-8B-Instruct_linear with Docker Model Runner:
```
docker model run hf.co/johnsutor/Llama-3-8B-Instruct_linear
```

Model Merge Parameters

Base model: meta-llama/Meta-Llama-3-8B-Instruct Models: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct DeepMount00/Llama-3-8b-Ita nbeerbower/llama-3-gutenberg-8B jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0 meta-llama/Meta-Llama-3-8B-Instruct Merge method: linear Random seed: 42 normalize: true weight: 1.0

Downloads last month: 15

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for johnsutor/Llama-3-8B-Instruct_linear

DeepMount00/Llama-3-8b-Ita

VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct

failspy/Meta-Llama-3-8B-Instruct-abliterated-v3

jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0

meta-llama/Meta-Llama-3-8B-Instruct

nbeerbower/llama-3-gutenberg-8B

Merge model

this model