Instructions to use abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq with Transformers:

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

pipe = pipeline("text-generation", model="abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhinavkulkarni/tiiuae-falcon-40b-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/tiiuae-falcon-40b-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/tiiuae-falcon-40b-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq

SGLang

How to use abhinavkulkarni/tiiuae-falcon-40b-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/tiiuae-falcon-40b-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/tiiuae-falcon-40b-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/tiiuae-falcon-40b-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/tiiuae-falcon-40b-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq with Docker Model Runner:
```
docker model run hf.co/abhinavkulkarni/tiiuae-falcon-40b-instruct-w4-g128-awq
```

Abhinav Kulkarni commited on Jul 19, 2023

Commit

4c60206

1 Parent(s): c6982b5

Updated README

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -51,6 +51,7 @@ config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
 # Tokenizer
 tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
 streamer = TextStreamer(tokenizer, skip_special_tokens=True)
 # Model
 w_bit = 4
@@ -96,6 +97,7 @@ output = model.generate(
     top_k=0,
     repetition_penalty=1.1,
     eos_token_id=tokenizer.eos_token_id,
     streamer=streamer,
 )
 ```

 # Tokenizer
 tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
 streamer = TextStreamer(tokenizer, skip_special_tokens=True)
+streamer = TextStreamer(tokenizer, skip_special_tokens=True)
 # Model
 w_bit = 4
     top_k=0,
     repetition_penalty=1.1,
     eos_token_id=tokenizer.eos_token_id,
+streamer=streamer,
     streamer=streamer,
 )
 ```