Instructions to use llmware/slim-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmware/slim-sentiment with Transformers:

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

pipe = pipeline("text-generation", model="llmware/slim-sentiment")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llmware/slim-sentiment with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llmware/slim-sentiment

SGLang

How to use llmware/slim-sentiment 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 "llmware/slim-sentiment" \
    --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": "llmware/slim-sentiment",
		"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 "llmware/slim-sentiment" \
        --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": "llmware/slim-sentiment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmware/slim-sentiment with Docker Model Runner:
```
docker model run hf.co/llmware/slim-sentiment
```

doberst commited on Feb 6, 2024

Commit

44fa5e7

verified ·

1 Parent(s): 5ae662e

Update README.md

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@@ -14,23 +14,26 @@ slim-sentiment has been fine-tuned for **sentiment analysis** function calls, ge
 `{"sentiment": ["positive"]}`
-SLIM models aspire re-imagine traditional 'hard-coded' classifiers through the use of function calls, to provide a flexible natural language generative model that can be used as decision gates and processing steps in a complex LLM-based automation workflow.
 Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool).
 ## Prompt format:
 `"<human> " + {text} + "\n" + `
-`"<{function}> " + {keys} + "</{function}>"`
 `+ "/n<bot>:" `
 <details>
-<summary><b> Transformers Script </b> </summary>
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
@@ -72,7 +75,7 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'*
-<summary><b>Using as Function Call in LLMWare</b></summary>
     from llmware.models import ModelCatalog
     slim_model = ModelCatalog().load_model("llmware/slim-sentiment")

 `{"sentiment": ["positive"]}`
+SLIM models are designed to provide a flexible natural language generative model that can be used for decision gates and processing steps in a complex LLM-based automation workflow.
 Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool).
 ## Prompt format:
+`function = "classify"`
+`params = "sentiment"`
 `"<human> " + {text} + "\n" + `
+`"<{function}> " + {params} + "</{function}>"`
 `+ "/n<bot>:" `
 <details>
+<summary>Transformers Script </summary>
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
+<summary>Using as Function Call in LLMWare</summary>
     from llmware.models import ModelCatalog
     slim_model = ModelCatalog().load_model("llmware/slim-sentiment")