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

Libraries

How to use llmware/slim-qa-gen-tiny-tool with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("llmware/slim-qa-gen-tiny-tool", device_map="auto")

llama-cpp-python

How to use llmware/slim-qa-gen-tiny-tool with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="llmware/slim-qa-gen-tiny-tool",
	filename="qa_gen_v3.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llmware/slim-qa-gen-tiny-tool with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmware/slim-qa-gen-tiny-tool
# Run inference directly in the terminal:
llama cli -hf llmware/slim-qa-gen-tiny-tool

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmware/slim-qa-gen-tiny-tool
# Run inference directly in the terminal:
llama cli -hf llmware/slim-qa-gen-tiny-tool

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf llmware/slim-qa-gen-tiny-tool
# Run inference directly in the terminal:
./llama-cli -hf llmware/slim-qa-gen-tiny-tool

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf llmware/slim-qa-gen-tiny-tool
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmware/slim-qa-gen-tiny-tool

Use Docker

docker model run hf.co/llmware/slim-qa-gen-tiny-tool

LM Studio
Jan
Ollama
How to use llmware/slim-qa-gen-tiny-tool with Ollama:
```
ollama run hf.co/llmware/slim-qa-gen-tiny-tool
```

Unsloth Studio

How to use llmware/slim-qa-gen-tiny-tool with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmware/slim-qa-gen-tiny-tool to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmware/slim-qa-gen-tiny-tool to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llmware/slim-qa-gen-tiny-tool to start chatting

Atomic Chat new
Docker Model Runner
How to use llmware/slim-qa-gen-tiny-tool with Docker Model Runner:
```
docker model run hf.co/llmware/slim-qa-gen-tiny-tool
```

Lemonade

How to use llmware/slim-qa-gen-tiny-tool with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmware/slim-qa-gen-tiny-tool

Run and chat with the model

lemonade run user.slim-qa-gen-tiny-tool-{{QUANT_TAG}}

List all available models

lemonade list

doberst commited on May 27, 2024

Commit

92267d7

verified ·

1 Parent(s): 292deda

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -5

README.md CHANGED Viewed

@@ -13,12 +13,17 @@ This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') func
      `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `
-The model has been designed to accept one of three different parameters to guide the type of question-answer created:  'question, answer' (generates a standard question and answer), 'boolean' (generates a 'yes-no' question and answer), and 'multiple choice' (generates a multiple choice question and answer).
-slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see slim-qa-gen-phi-3-tool, which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.
-[**slim-qa-gen-tiny*](https://huggingface.co/llmware/slim-qa-gen-tiny) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
 To pull the model via API:
@@ -32,14 +37,14 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
     from llmware.models import ModelCatalog
     # to load the model and make a basic inference
-    model = ModelCatalog().load_model("slim-qa-gen-tiny-tool")
     response = model.function_call(text_sample)
     # this one line will download the model and run a series of tests
     ModelCatalog().tool_test_run("slim-qa-gen-tiny-tool", verbose=True)
-Note: please review [**config.json**](https://huggingface.co/llmware/slim-xsum-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
 ## Model Card Contact

      `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `
+The model has been designed to accept one of three different parameters to guide the type of question-answer created:
+  -- 'question, answer' (generates a standard question and answer),
+  -- 'boolean' (generates a 'yes-no' question and answer), and
+  -- 'multiple choice' (generates a multiple choice question and answer).
+slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see [slim-qa-gen-phi-3-tool](https://huggingface.co/llmware/slim-qa-gen-phi-3-tool), which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.
+[**slim-qa-gen-tiny**](https://huggingface.co/llmware/slim-qa-gen-tiny) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
 To pull the model via API:
     from llmware.models import ModelCatalog
     # to load the model and make a basic inference
+    model = ModelCatalog().load_model("slim-qa-gen-tiny-tool", temperature=0.5, sample=True)
     response = model.function_call(text_sample)
     # this one line will download the model and run a series of tests
     ModelCatalog().tool_test_run("slim-qa-gen-tiny-tool", verbose=True)
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-qa-gen-tiny-tool/blob/main/config.json) in the repository for prompt template information, details on the model, and full test set.
 ## Model Card Contact