Instructions to use devorein/llama_7b-instruct_lora_int4-subj_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devorein/llama_7b-instruct_lora_int4-subj_eval with Transformers:

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

pipe = pipeline("text-generation", model="devorein/llama_7b-instruct_lora_int4-subj_eval")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devorein/llama_7b-instruct_lora_int4-subj_eval")
model = AutoModelForCausalLM.from_pretrained("devorein/llama_7b-instruct_lora_int4-subj_eval")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use devorein/llama_7b-instruct_lora_int4-subj_eval with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devorein/llama_7b-instruct_lora_int4-subj_eval"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devorein/llama_7b-instruct_lora_int4-subj_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/devorein/llama_7b-instruct_lora_int4-subj_eval

SGLang

How to use devorein/llama_7b-instruct_lora_int4-subj_eval 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 "devorein/llama_7b-instruct_lora_int4-subj_eval" \
    --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": "devorein/llama_7b-instruct_lora_int4-subj_eval",
		"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 "devorein/llama_7b-instruct_lora_int4-subj_eval" \
        --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": "devorein/llama_7b-instruct_lora_int4-subj_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use devorein/llama_7b-instruct_lora_int4-subj_eval with Docker Model Runner:
```
docker model run hf.co/devorein/llama_7b-instruct_lora_int4-subj_eval
```

Adding `safetensors` variant of this model

by SFconvertbot - opened Jan 24, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

Files changed (1) hide show

model.safetensors +3 -0

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:bf5ba07b315442a47320ed02b92ad624a426e45046e2788830528ece336e9b45
+size 3910781056