Instructions to use relaxml/Llama-2-7b-E8P-2Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use relaxml/Llama-2-7b-E8P-2Bit with Transformers:

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

pipe = pipeline("text-generation", model="relaxml/Llama-2-7b-E8P-2Bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("relaxml/Llama-2-7b-E8P-2Bit")
model = AutoModelForCausalLM.from_pretrained("relaxml/Llama-2-7b-E8P-2Bit")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use relaxml/Llama-2-7b-E8P-2Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "relaxml/Llama-2-7b-E8P-2Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "relaxml/Llama-2-7b-E8P-2Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/relaxml/Llama-2-7b-E8P-2Bit

SGLang

How to use relaxml/Llama-2-7b-E8P-2Bit 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 "relaxml/Llama-2-7b-E8P-2Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "relaxml/Llama-2-7b-E8P-2Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "relaxml/Llama-2-7b-E8P-2Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "relaxml/Llama-2-7b-E8P-2Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use relaxml/Llama-2-7b-E8P-2Bit with Docker Model Runner:
```
docker model run hf.co/relaxml/Llama-2-7b-E8P-2Bit
```

Some weights of the model checkpoint at relaxml/Llama-2-7b-E8P-2Bit were not used when initializing LlamaForCausalLM

by pkhara - opened Oct 7, 2025

Discussion

pkhara

Oct 7, 2025

Some weights of the model checkpoint at relaxml/Llama-2-7b-E8P-2Bit were not used when initializing LlamaForCausalLM:

This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture

This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)

Some weights of LlamaForCausalLM were not initialized from the model checkpoint at relaxml/Llama-2-7b-E8P-2Bit and are newly initialized:

You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

How to solve this issue?

My code:

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("relaxml/Llama-2-7b-E8P-2Bit")
model = AutoModelForCausalLM.from_pretrained("relaxml/Llama-2-7b-E8P-2Bit")

pkhara

Oct 8, 2025

@at676 Can you check the issue and please help here?

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