Instructions to use minkdank/QWEN-JSON-data-extration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minkdank/QWEN-JSON-data-extration with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="minkdank/QWEN-JSON-data-extration",
	filename="Qwen2.5-3B.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use minkdank/QWEN-JSON-data-extration with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf minkdank/QWEN-JSON-data-extration:Q8_0
# Run inference directly in the terminal:
llama-cli -hf minkdank/QWEN-JSON-data-extration:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf minkdank/QWEN-JSON-data-extration:Q8_0
# Run inference directly in the terminal:
llama-cli -hf minkdank/QWEN-JSON-data-extration:Q8_0

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 minkdank/QWEN-JSON-data-extration:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf minkdank/QWEN-JSON-data-extration:Q8_0

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 minkdank/QWEN-JSON-data-extration:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf minkdank/QWEN-JSON-data-extration:Q8_0

Use Docker

docker model run hf.co/minkdank/QWEN-JSON-data-extration:Q8_0

LM Studio
Jan
Ollama
How to use minkdank/QWEN-JSON-data-extration with Ollama:
```
ollama run hf.co/minkdank/QWEN-JSON-data-extration:Q8_0
```

Unsloth Studio

How to use minkdank/QWEN-JSON-data-extration 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 minkdank/QWEN-JSON-data-extration 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 minkdank/QWEN-JSON-data-extration to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for minkdank/QWEN-JSON-data-extration to start chatting

How to use minkdank/QWEN-JSON-data-extration with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf minkdank/QWEN-JSON-data-extration:Q8_0

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": "minkdank/QWEN-JSON-data-extration:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use minkdank/QWEN-JSON-data-extration with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf minkdank/QWEN-JSON-data-extration:Q8_0

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 minkdank/QWEN-JSON-data-extration:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use minkdank/QWEN-JSON-data-extration with Docker Model Runner:
```
docker model run hf.co/minkdank/QWEN-JSON-data-extration:Q8_0
```

Lemonade

How to use minkdank/QWEN-JSON-data-extration with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull minkdank/QWEN-JSON-data-extration:Q8_0

Run and chat with the model

lemonade run user.QWEN-JSON-data-extration-Q8_0

List all available models

lemonade list

minkdank commited on Nov 18, 2025

Commit

7de19d9

verified ·

1 Parent(s): 1ec135a

Trained with Unsloth - config

Browse files

Files changed (1) hide show

config.json +68 -67

config.json CHANGED Viewed

@@ -1,68 +1,69 @@
 {
-    "architectures": [
-        "Qwen2ForCausalLM"
-    ],
-    "attention_dropout": 0.0,
-    "eos_token_id": 151645,
-    "hidden_act": "silu",
-    "hidden_size": 2048,
-    "initializer_range": 0.02,
-    "intermediate_size": 11008,
-    "layer_types": [
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention"
-    ],
-    "max_position_embeddings": 32768,
-    "max_window_layers": 70,
-    "model_type": "qwen2",
-    "num_attention_heads": 16,
-    "num_hidden_layers": 36,
-    "num_key_value_heads": 2,
-    "pad_token_id": 151654,
-    "rms_norm_eps": 1e-06,
-    "rope_scaling": null,
-    "rope_theta": 1000000.0,
-    "sliding_window": null,
-    "tie_word_embeddings": true,
-    "torch_dtype": "bfloat16",
-    "transformers_version": "4.55.4",
-    "unsloth_fixed": true,
-    "unsloth_version": "2025.11.2",
-    "use_cache": true,
-    "use_sliding_window": false,
-    "vocab_size": 151936
-}

 {
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.4",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.11.3",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}