Instructions to use llama-farm/fda-task-classifier-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llama-farm/fda-task-classifier-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="llama-farm/fda-task-classifier-gguf",
	filename="model.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use llama-farm/fda-task-classifier-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llama-farm/fda-task-classifier-gguf
# Run inference directly in the terminal:
llama-cli -hf llama-farm/fda-task-classifier-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llama-farm/fda-task-classifier-gguf
# Run inference directly in the terminal:
llama-cli -hf llama-farm/fda-task-classifier-gguf

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 llama-farm/fda-task-classifier-gguf
# Run inference directly in the terminal:
./llama-cli -hf llama-farm/fda-task-classifier-gguf

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 llama-farm/fda-task-classifier-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llama-farm/fda-task-classifier-gguf

Use Docker

docker model run hf.co/llama-farm/fda-task-classifier-gguf

LM Studio
Jan

vLLM

How to use llama-farm/fda-task-classifier-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llama-farm/fda-task-classifier-gguf

Ollama
How to use llama-farm/fda-task-classifier-gguf with Ollama:
```
ollama run hf.co/llama-farm/fda-task-classifier-gguf
```

Unsloth Studio new

How to use llama-farm/fda-task-classifier-gguf 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 llama-farm/fda-task-classifier-gguf 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 llama-farm/fda-task-classifier-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llama-farm/fda-task-classifier-gguf to start chatting

Docker Model Runner
How to use llama-farm/fda-task-classifier-gguf with Docker Model Runner:
```
docker model run hf.co/llama-farm/fda-task-classifier-gguf
```

Lemonade

How to use llama-farm/fda-task-classifier-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llama-farm/fda-task-classifier-gguf

Run and chat with the model

lemonade run user.fda-task-classifier-gguf-{{QUANT_TAG}}

List all available models

lemonade list

fda-task-classifier-gguf

File size: 1,312 Bytes

5f727a0

# Modelfile for FDA Task Classifier
# Specialized model for identifying and extracting FDA regulatory tasks from document chunks

FROM ./model.gguf

# Model parameters
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 4096
PARAMETER num_predict 512

# System message to guide the model's task extraction behavior
SYSTEM """You are an FDA regulatory task extraction specialist. Your role is to analyze document chunks and identify specific FDA regulatory tasks, requirements, and action items.

When analyzing text, focus on:
- Regulatory submissions and deadlines
- Clinical trial requirements
- Manufacturing and quality control tasks
- Compliance and reporting obligations
- Safety monitoring requirements
- Documentation and record-keeping tasks

Extract tasks in a structured format with:
- Task description
- Regulatory category (e.g., clinical, manufacturing, compliance)
- Priority level if mentioned
- Deadline if specified
- Relevant FDA regulation references

Be precise and factual. Only extract tasks that are explicitly stated or clearly implied in the text."""

# Template for structured output
TEMPLATE """{{ if .System }}<|system|>
{{ .System }}</|system|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}</|user|>
{{ end }}<|assistant|>
{{ .Response }}<|end|>"""