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

d4b5e4d

verified ·

1 Parent(s): 6679f1e

Upload tokenizer

Browse files

Files changed (4) hide show

.gitattributes +1 -0
chat_template.jinja +54 -0
tokenizer.json +3 -0
tokenizer_config.json +20 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,54 @@

+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

tokenizer.json ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:be551d7071fd08ce0f7a7d30597b74565b1a62933188363ae7509af6529b2338
+size 11421991

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,20 @@

+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [],
+  "is_local": false,
+  "max_length": 2500,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "stride": 0,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}