Instructions to use constehub/rag-evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use constehub/rag-evaluation with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("constehub/rag-evaluation", dtype="auto")

llama-cpp-python

How to use constehub/rag-evaluation with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="constehub/rag-evaluation",
	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 constehub/rag-evaluation with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf constehub/rag-evaluation:Q8_0
# Run inference directly in the terminal:
llama-cli -hf constehub/rag-evaluation:Q8_0

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/constehub/rag-evaluation:Q8_0

LM Studio
Jan

vLLM

How to use constehub/rag-evaluation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/constehub/rag-evaluation:Q8_0

SGLang

How to use constehub/rag-evaluation 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 "constehub/rag-evaluation" \
    --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": "constehub/rag-evaluation",
		"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 "constehub/rag-evaluation" \
        --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": "constehub/rag-evaluation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use constehub/rag-evaluation with Ollama:
```
ollama run hf.co/constehub/rag-evaluation:Q8_0
```

Unsloth Studio new

How to use constehub/rag-evaluation 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 constehub/rag-evaluation 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 constehub/rag-evaluation to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for constehub/rag-evaluation to start chatting

Pi new

How to use constehub/rag-evaluation with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf constehub/rag-evaluation: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": "constehub/rag-evaluation:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use constehub/rag-evaluation with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf constehub/rag-evaluation: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 constehub/rag-evaluation:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use constehub/rag-evaluation with Docker Model Runner:
```
docker model run hf.co/constehub/rag-evaluation:Q8_0
```

Lemonade

How to use constehub/rag-evaluation with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull constehub/rag-evaluation:Q8_0

Run and chat with the model

lemonade run user.rag-evaluation-Q8_0

List all available models

lemonade list

RAG Context Evaluator - Qwen3-8B Fine-tuned 🚀

Model Details 📋

License: apache-2.0
Finetuned from model: unsloth/qwen3-8b-unsloth-bnb-4bit
Model type: Text Generation (Specialized for RAG Evaluation)
Quantization: Q8_0

Model Description 🎯

This model is specifically fine-tuned to evaluate the quality of retrieved contexts in Retrieval-Augmented Generation (RAG) systems. It assesses retrieved passages against user queries using multiple evaluation metrics commonly used in information retrieval and RAG evaluation.

Intended Uses 💡

Primary Use Case 🎯

RAG System Evaluation: Automatically assess the quality of retrieved contexts for question-answering systems
Information Retrieval Quality Control: Evaluate how well retrieved documents match user queries
Academic Research: Support research in information retrieval and RAG system optimization

Evaluation Metrics 📊

The model evaluates retrieved contexts using the following metrics:

Completeness 📝 - How thoroughly the retrieved context addresses the query
Clarity ✨ - How clear and understandable the retrieved information is
Conciseness 🎪 - How efficiently the information is presented without redundancy
Precision 🎯 - How accurate and relevant the retrieved information is
Recall 🔍 - How comprehensive the retrieved information is in covering the query
MRR (Mean Reciprocal Rank) 📈 - Ranking quality of relevant results
NDCG (Normalized Discounted Cumulative Gain) 📊 - Ranking quality with position consideration
Relevance 🔗 - Overall relevance of retrieved contexts to the query

Training Data 📚

https://huggingface.co/datasets/constehub/rag-evaluation-dataset

Example Training Instance

{
  "instruction": "Evaluate the agent's response according to the metrics: completeness, clarity, conciseness, precision, recall, mrr, ndcg, relevance",
  "input": {
    "question": "Question about retrieved context",
    "retrieved_contexts": "[Multiple numbered passages with source citations]"
  },
  "output": [
    {
      "name": "completeness",
      "value": 1,
      "comment": "Detailed evaluation comment"
    }
    // ... other metrics
  ]
}

Performance and Limitations ⚡

Strengths

Specialized for RAG evaluation
Multi-dimensional assessment capability
Detailed explanatory comments for each metric

Limitations

Context Length: Performance may vary with very long retrieved contexts

Ethical Considerations 🤝

The model should be used as a tool to assist human evaluators, not replace human judgment entirely
Evaluations should be validated by domain experts for critical applications

Technical Specifications 🔧

Base Model: Qwen3-8B
Quantization: Q8_0

Usage Example 💻

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "mendrika261/rag-evaluator-qwen3-8b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example evaluation prompt
prompt = """Evaluate the agent's response according to the metrics: completeness, clarity, conciseness, precision, recall, mrr, ndcg, relevance

Question: [Your question here]
Retrieved contexts: [Your retrieved contexts here]"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
evaluation = tokenizer.decode(outputs[0], skip_special_tokens=True)

Citation 📄

If you use this model in your research, please cite:

@misc{constehub-rag-evaluator,
  title={RAG Context Evaluator - Qwen3-8B Fine-tuned},
  author={constehub},
  year={2025},
  howpublished={\url{https://huggingface.co/constehub/rag-evaluation}}
}

Contact 📧

For questions or issues regarding this model, please contact the developer through the Hugging Face model repository.

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 3

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

8-bit

Evaluation results

Multi-metric Assessment
self-reported

0-5