Instructions to use pawlaszc/DigitalForensicsText2SQLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pawlaszc/DigitalForensicsText2SQLite with Transformers:

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

pipe = pipeline("text-generation", model="pawlaszc/DigitalForensicsText2SQLite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pawlaszc/DigitalForensicsText2SQLite")
model = AutoModelForCausalLM.from_pretrained("pawlaszc/DigitalForensicsText2SQLite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use pawlaszc/DigitalForensicsText2SQLite with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pawlaszc/DigitalForensicsText2SQLite",
	filename="forensic-sqlite-llama-3.2-3b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Local Apps Settings

llama.cpp

How to use pawlaszc/DigitalForensicsText2SQLite 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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

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 pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Use Docker

docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

LM Studio
Jan

vLLM

How to use pawlaszc/DigitalForensicsText2SQLite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pawlaszc/DigitalForensicsText2SQLite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pawlaszc/DigitalForensicsText2SQLite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

SGLang

How to use pawlaszc/DigitalForensicsText2SQLite 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 "pawlaszc/DigitalForensicsText2SQLite" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pawlaszc/DigitalForensicsText2SQLite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "pawlaszc/DigitalForensicsText2SQLite" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pawlaszc/DigitalForensicsText2SQLite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pawlaszc/DigitalForensicsText2SQLite with Ollama:
```
ollama run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
```

Unsloth Studio

How to use pawlaszc/DigitalForensicsText2SQLite 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 pawlaszc/DigitalForensicsText2SQLite 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 pawlaszc/DigitalForensicsText2SQLite to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pawlaszc/DigitalForensicsText2SQLite to start chatting

How to use pawlaszc/DigitalForensicsText2SQLite with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pawlaszc/DigitalForensicsText2SQLite:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pawlaszc/DigitalForensicsText2SQLite with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use pawlaszc/DigitalForensicsText2SQLite with Docker Model Runner:
```
docker model run hf.co/pawlaszc/DigitalForensicsText2SQLite:Q4_K_M
```

Lemonade

How to use pawlaszc/DigitalForensicsText2SQLite with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pawlaszc/DigitalForensicsText2SQLite:Q4_K_M

Run and chat with the model

lemonade run user.DigitalForensicsText2SQLite-Q4_K_M

List all available models

lemonade list

pawlaszc commited on Mar 12

Commit

3bd524d

verified ·

1 Parent(s): 4b4ad8b

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -78,7 +78,7 @@ analysis tool.
 |---|---|
 | **Overall Accuracy** | **93.0%** (93/100) |
 | 95% CI (Wilson) | [86.3%, 96.6%] |
-| Executable Queries | 92/100 |
 | GPT-4o Accuracy | 95.0% (gap: 4 pp, p ≈ 0.39) |
 | Base Model (no fine-tuning) | 35.0% |
 | Improvement over base | +56 pp |
@@ -130,7 +130,7 @@ configuration without app name is recommended for general use.
 |---|---|---|---|
 | Base model (no fine-tuning) | — | 35.0% | — |
 | Fine-tuned, no augmentation | — | 68.0% | +33 pp |
-| + Data augmentation (3.4×) | — | 74.0% | +6 pp |
 | + Extended training (7 epochs) | 0.3617 | 92.0% | +10 pp |
 | + Post-processing pipeline | 0.3617 | 87.0% | +3 pp |
 | + Execution feedback | 0.3617 | 90.0% | +3 pp |
@@ -246,7 +246,7 @@ class ForensicSQLGenerator:
             "SQLite Query:\n"
         )
         inputs = self.tokenizer(
-            prompt, return_tensors="pt", truncation=True, max_length=2048
         )
         inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
         input_length = inputs["input_ids"].shape[1]
@@ -318,7 +318,7 @@ ollama run forensic-sql
 | Learning rate | 2e-5 (peak) |
 | LR scheduler | Cosine with warmup |
 | Batch size | 1 + gradient accumulation 4 |
-| Max sequence length | 2048 |
 | Optimizer | AdamW |
 | Hardware | Apple M-series, 16 GB unified memory |
 | Training time | ~17.6 hours |

 |---|---|
 | **Overall Accuracy** | **93.0%** (93/100) |
 | 95% CI (Wilson) | [86.3%, 96.6%] |
+| Executable Queries | 94/100 |
 | GPT-4o Accuracy | 95.0% (gap: 4 pp, p ≈ 0.39) |
 | Base Model (no fine-tuning) | 35.0% |
 | Improvement over base | +56 pp |
 |---|---|---|---|
 | Base model (no fine-tuning) | — | 35.0% | — |
 | Fine-tuned, no augmentation | — | 68.0% | +33 pp |
+| + Data augmentation (2.4×) | — | 74.0% | +6 pp |
 | + Extended training (7 epochs) | 0.3617 | 92.0% | +10 pp |
 | + Post-processing pipeline | 0.3617 | 87.0% | +3 pp |
 | + Execution feedback | 0.3617 | 90.0% | +3 pp |
             "SQLite Query:\n"
         )
         inputs = self.tokenizer(
+            prompt, return_tensors="pt", truncation=True, max_length=4096
         )
         inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
         input_length = inputs["input_ids"].shape[1]
 | Learning rate | 2e-5 (peak) |
 | LR scheduler | Cosine with warmup |
 | Batch size | 1 + gradient accumulation 4 |
+| Max sequence length | 4096 |
 | Optimizer | AdamW |
 | Hardware | Apple M-series, 16 GB unified memory |
 | Training time | ~17.6 hours |