Instructions to use TheBloke/Falcon-7B-Instruct-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Falcon-7B-Instruct-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Falcon-7B-Instruct-GPTQ", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TheBloke/Falcon-7B-Instruct-GPTQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TheBloke/Falcon-7B-Instruct-GPTQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheBloke/Falcon-7B-Instruct-GPTQ

SGLang

How to use TheBloke/Falcon-7B-Instruct-GPTQ 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 "TheBloke/Falcon-7B-Instruct-GPTQ" \
    --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": "TheBloke/Falcon-7B-Instruct-GPTQ",
		"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 "TheBloke/Falcon-7B-Instruct-GPTQ" \
        --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": "TheBloke/Falcon-7B-Instruct-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/Falcon-7B-Instruct-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/Falcon-7B-Instruct-GPTQ
```

The model 'RWGPTQForCausalLM' is not supported for text-generation.

#18

by herMaster - opened Nov 20, 2023

Discussion

herMaster

Nov 20, 2023

After loading the model and tokenizer, I set text generation as the task in the pipeline. After loading the model, its description is -

RWGPTQForCausalLM(
(model): RWForCausalLM(
(transformer): RWModel(
(word_embeddings): Embedding(65024, 4544)
(h): ModuleList(
(0-31): 32 x DecoderLayer(
(input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
(self_attention): Attention(
(maybe_rotary): RotaryEmbedding()
(attention_dropout): Dropout(p=0.0, inplace=False)
(dense): GeneralQuantLinear(in_features=4544, out_features=4544, bias=True)
(query_key_value): GeneralQuantLinear(in_features=4544, out_features=4672, bias=True)
)
(mlp): MLP(
(act): GELU(approximate='none')
(dense_4h_to_h): GeneralQuantLinear(in_features=18176, out_features=4544, bias=True)
(dense_h_to_4h): GeneralQuantLinear(in_features=4544, out_features=18176, bias=True)
)
)
)
(ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=4544, out_features=65024, bias=False)
)
)

But when we set text generation as the task in the pipeline, the colab gives an error mentioned in the Title.

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