Instructions to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Transformers:

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

pipe = pipeline("text-generation", model="nimendraai/NuExtract-tiny-Resume-Data-Extractor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nimendraai/NuExtract-tiny-Resume-Data-Extractor")
model = AutoModelForCausalLM.from_pretrained("nimendraai/NuExtract-tiny-Resume-Data-Extractor")
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 nimendraai/NuExtract-tiny-Resume-Data-Extractor with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nimendraai/NuExtract-tiny-Resume-Data-Extractor",
	filename="NuExtract-tiny-v1.5.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor: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 nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor: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 nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

Use Docker

docker model run hf.co/nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

LM Studio
Jan

vLLM

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

SGLang

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor 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 "nimendraai/NuExtract-tiny-Resume-Data-Extractor" \
    --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": "nimendraai/NuExtract-tiny-Resume-Data-Extractor",
		"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 "nimendraai/NuExtract-tiny-Resume-Data-Extractor" \
        --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": "nimendraai/NuExtract-tiny-Resume-Data-Extractor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Ollama:
```
ollama run hf.co/nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
```

Unsloth Studio new

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor 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 nimendraai/NuExtract-tiny-Resume-Data-Extractor 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 nimendraai/NuExtract-tiny-Resume-Data-Extractor to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nimendraai/NuExtract-tiny-Resume-Data-Extractor to start chatting

Pi new

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor: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": "nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nimendraai/NuExtract-tiny-Resume-Data-Extractor: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 nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Docker Model Runner:
```
docker model run hf.co/nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M
```

Lemonade

How to use nimendraai/NuExtract-tiny-Resume-Data-Extractor with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nimendraai/NuExtract-tiny-Resume-Data-Extractor:Q4_K_M

Run and chat with the model

lemonade run user.NuExtract-tiny-Resume-Data-Extractor-Q4_K_M

List all available models

lemonade list

nimendraai commited on Apr 22

Commit

6170050

verified ·

1 Parent(s): 8169647

Trained with Unsloth - config

Browse files

Files changed (1) hide show

config.json +58 -0

config.json ADDED Viewed

	@@ -0,0 +1,58 @@

+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151643,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "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"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 24,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151643,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_version": "2026.4.6",
+    "use_cache": false,
+    "use_mrope": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}