Instructions to use C0ldSmi1e/resume-reader-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use C0ldSmi1e/resume-reader-best with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="C0ldSmi1e/resume-reader-best",
	filename="unsloth.Q8_0.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 C0ldSmi1e/resume-reader-best with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf C0ldSmi1e/resume-reader-best:Q8_0
# Run inference directly in the terminal:
llama-cli -hf C0ldSmi1e/resume-reader-best:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf C0ldSmi1e/resume-reader-best:Q8_0
# Run inference directly in the terminal:
llama-cli -hf C0ldSmi1e/resume-reader-best: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 C0ldSmi1e/resume-reader-best:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf C0ldSmi1e/resume-reader-best: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 C0ldSmi1e/resume-reader-best:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf C0ldSmi1e/resume-reader-best:Q8_0

Use Docker

docker model run hf.co/C0ldSmi1e/resume-reader-best:Q8_0

LM Studio
Jan

vLLM

How to use C0ldSmi1e/resume-reader-best with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "C0ldSmi1e/resume-reader-best"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "C0ldSmi1e/resume-reader-best",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/C0ldSmi1e/resume-reader-best:Q8_0

Ollama
How to use C0ldSmi1e/resume-reader-best with Ollama:
```
ollama run hf.co/C0ldSmi1e/resume-reader-best:Q8_0
```

Unsloth Studio new

How to use C0ldSmi1e/resume-reader-best 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 C0ldSmi1e/resume-reader-best 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 C0ldSmi1e/resume-reader-best to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for C0ldSmi1e/resume-reader-best to start chatting

Docker Model Runner
How to use C0ldSmi1e/resume-reader-best with Docker Model Runner:
```
docker model run hf.co/C0ldSmi1e/resume-reader-best:Q8_0
```

Lemonade

How to use C0ldSmi1e/resume-reader-best with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull C0ldSmi1e/resume-reader-best:Q8_0

Run and chat with the model

lemonade run user.resume-reader-best-Q8_0

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model Card: Resume Information Extractor (LLM-based)

Overview

This model is a distilled, instruction-tuned version of the DeepSeek-R1-Distill-Llama-8B language model, optimized for extracting structured information from resumes in English. It was built using the Unsloth library for efficient fine-tuning and inference.

Given a raw resume text, the model outputs structured JSON containing:

skills: list of skills mentioned
education: simplified school-degree-major format
experience: list of job roles

Intended Uses

This model is designed for:

HR software to parse applicant resumes automatically
Applicant tracking systems (ATS)
AI assistants helping with recruiting and screening
EdTech or job board platforms classifying user profiles

Example Input Prompt:

You are an experienced HR and now you will review a resume then extract key information from it.

# Input
Here is the resume text:
[PASTE RESUME TEXT HERE]

### Response
<think>

Expected Output:

{
  "skills": [...],
  "education": [...],
  "experience": [...]
}

Training & Technical Details

Base model: unsloth/DeepSeek-R1-Distill-Llama-8B
Library: Unsloth with support for 4-bit quantization (bitsandbytes)
Fine-tuning style: Instruction-tuning using formatted HR task prompts
Max sequence length: 8096 tokens
Hardware requirements: ~16GB GPU RAM (with 4-bit loading)

Limitations

Performance may degrade with non-English or poorly formatted resumes
Only extracts roles (not company names or dates)
Cannot handle multi-lingual documents
Does not validate output schema; use external validators if needed

Citation

If you use this model, please cite the following components:

Unsloth: https://github.com/unslothai/unsloth
DeepSeek LLM: https://github.com/deepseek-ai

License

Apache 2.0

Downloads last month: 30

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit