Instructions to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with Transformers:

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

pipe = pipeline("text-generation", model="abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")
model = AutoModelForMultimodalLM.from_pretrained("abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq

SGLang

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq 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 "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq" \
    --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": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq",
		"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 "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq" \
        --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": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with Docker Model Runner:
```
docker model run hf.co/abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq
```

Abhinav Kulkarni commited on Jul 14, 2023

Commit

b3f236e

1 Parent(s): e885f32

Updated README

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -31,9 +31,9 @@ For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1
 ## How to Use
 ```bash
-git clone https://github.com/mit-han-lab/llm-awq \
 && cd llm-awq \
-&& git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
 && pip install -e . \
 && cd awq/kernels \
 && python setup.py install
@@ -44,9 +44,9 @@ import torch
 from awq.quantize.quantizer import real_quantize_model_weight
 from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-from huggingface_hub import hf_hub_download
-model_name = "VMware/open-llama-7b-open-instruct"
 # Config
 config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
@@ -61,7 +61,7 @@ q_config = {
     "q_group_size": 128,
 }
-load_quant = hf_hub_download('abhinavkulkarni/VMWare-open-llama-7b-open-instruct-w4-g128-awq', 'pytorch_model.bin')
 with init_empty_weights():
     model = AutoModelForCausalLM.from_config(config=config,

 ## How to Use
 ```bash
+git clone https://github.com/abhinavkulkarni/llm-awq \
 && cd llm-awq \
+&& git checkout e977c5a570c5048b67a45b1eb823b81de02d0d60 \
 && pip install -e . \
 && cd awq/kernels \
 && python setup.py install
 from awq.quantize.quantizer import real_quantize_model_weight
 from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+model_name = "abhinavkulkarni/VMware-open-llama-7b-open-instruct"
 # Config
 config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
     "q_group_size": 128,
 }
+load_quant = snapshot_download(model_name)
 with init_empty_weights():
     model = AutoModelForCausalLM.from_config(config=config,