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

16811d7

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1 Parent(s): 7d6b92f

Update README.md

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README.md +13 -11

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@@ -7,24 +7,27 @@ inference: false
 <!-- Provide a quick summary of what the model is/does. -->
-**slim-sentiment** is part of the SLIM ("Structured Language Instruction Model") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
 slim-sentiment has been fine-tuned for **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
 `{"sentiment": ["positive"]}`
-SLIM models re-imagine traditional 'hard-coded' classifiers through the use of function calls, and to provide a natural language flexible tool that can be used as decision gates and processing steps in a complex LLM-based automation workflow.
-Each slim model has a corresponding '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> Getting Started with Transformers Script </b> </summary>
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
@@ -59,12 +62,11 @@ Each slim model has a corresponding 'quantized tool' version, e.g.,  [**'slim-se
     except:
         print("fail - could not convert to python dictionary automatically - ", llm_string_output)
-   </details>
-<details>
 <summary><b>Using as Function Call in LLMWare</b></summary>
 We envision the slim models deployed in a pipeline/workflow/templating framework that handles the prompt packaging more elegantly.

 <!-- Provide a quick summary of what the model is/does. -->
+**slim-sentiment** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
 slim-sentiment has been fine-tuned for **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
 `{"sentiment": ["positive"]}`
+SLIM models 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")
     except:
         print("fail - could not convert to python dictionary automatically - ", llm_string_output)
+   </details>
+<details>
 <summary><b>Using as Function Call in LLMWare</b></summary>
 We envision the slim models deployed in a pipeline/workflow/templating framework that handles the prompt packaging more elegantly.