Instructions to use QuantFactory/Lexora-Medium-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Lexora-Medium-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Lexora-Medium-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Lexora-Medium-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Lexora-Medium-7B-GGUF: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 QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Lexora-Medium-7B-GGUF: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 QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Lexora-Medium-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Lexora-Medium-7B-GGUF 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 QuantFactory/Lexora-Medium-7B-GGUF 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 QuantFactory/Lexora-Medium-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Lexora-Medium-7B-GGUF to start chatting

How to use QuantFactory/Lexora-Medium-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Lexora-Medium-7B-GGUF: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": "QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Lexora-Medium-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Lexora-Medium-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lexora-Medium-7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Lexora-Medium-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Lexora-Medium-7B-GGUF: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 QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Lexora-Medium-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Lexora-Medium-7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

aashish1904 commited on Oct 22, 2024

Commit

9e1a8d3

verified ·

1 Parent(s): 78ab502

Upload README.md with huggingface_hub

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README.md +53 -0

README.md ADDED Viewed

	@@ -0,0 +1,53 @@

+---
+library_name: transformers
+license: apache-2.0
+language:
+- it
+- en
+datasets:
+- DeepMount00/Sonnet-3.5-ITA-INSTRUCTION
+- DeepMount00/Sonnet-3.5-ITA-DPO
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Lexora-Medium-7B-GGUF
+This is quantized version of [DeepMount00/Lexora-Medium-7B](https://huggingface.co/DeepMount00/Lexora-Medium-7B) created using llama.cpp
+# Original Model Card
+## How to Use
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "DeepMount00/Lexora-Medium-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+prompt = [{'role': 'user', 'content': """Marco ha comprato 5 scatole di cioccolatini. Ogni scatola contiene 12 cioccolatini. Ha deciso di dare 3 cioccolatini a ciascuno dei suoi 7 amici. Quanti cioccolatini gli rimarranno dopo averli distribuiti ai suoi amici?"""}]
+inputs = tokenizer.apply_chat_template(
+    prompt,
+    add_generation_prompt=True,
+    return_tensors='pt'
+)
+tokens = model.generate(
+    inputs.to(model.device),
+    max_new_tokens=1024,
+    temperature=0.001,
+    do_sample=True
+)
+print(tokenizer.decode(tokens[0], skip_special_tokens=False))
+```