Llama-3-8B-Instruct-GPTQ-4-Bit-Debug

Instructions to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with Transformers:

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

pipe = pipeline("text-generation", model="davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug")
model = AutoModelForCausalLM.from_pretrained("davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug", device_map="auto")
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 davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug

SGLang

How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug 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 "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug" \
    --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": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"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 "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug" \
        --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": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with Docker Model Runner:
```
docker model run hf.co/davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug
```

davidxmle commited on Apr 30, 2024

Commit

4187b55

verified ·

1 Parent(s): 4cb77e2

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@@ -25,17 +25,6 @@ tags:
 datasets:
 - wikitext
 ---
-<!-- header start -->
-<!-- 200823 -->
-<div style="width: auto; margin-left: auto; margin-right: auto">
-<img src="https://www.astronomer.io/logo/astronomer-logo-RGB-standard-1200px.png" alt="Astronomer" style="width: 60%; min-width: 400px; display: block; margin: auto;">
-</div>
-<div style="margin-top: 1.0em; margin-bottom: 1.0em;"></div>
-<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">This model is generously created and made open source by <a href="https://astronomer.io">Astronomer</a>.</p></div>
-<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Astronomer is the de facto company for <a href="https://airflow.apache.org/">Apache Airflow</a>, the most trusted open-source framework for data orchestration and MLOps.</p></div>
-<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
-<!-- header end -->
 # Llama-3-8B-Instruct-GPTQ-4-Bit
 - Original Model creator: [Meta Llama from Meta](https://huggingface.co/meta-llama)

 datasets:
 - wikitext
 ---
 # Llama-3-8B-Instruct-GPTQ-4-Bit
 - Original Model creator: [Meta Llama from Meta](https://huggingface.co/meta-llama)