Instructions to use XeroCodes/lynx-8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use XeroCodes/lynx-8b-gguf with PEFT:
```
Task type is invalid.
```

How to use XeroCodes/lynx-8b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XeroCodes/lynx-8b-gguf",
	filename="lynx-8b-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use XeroCodes/lynx-8b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XeroCodes/lynx-8b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/lynx-8b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XeroCodes/lynx-8b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/lynx-8b-gguf:F16

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 XeroCodes/lynx-8b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf XeroCodes/lynx-8b-gguf:F16

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 XeroCodes/lynx-8b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XeroCodes/lynx-8b-gguf:F16

Use Docker

docker model run hf.co/XeroCodes/lynx-8b-gguf:F16

LM Studio
Jan

vLLM

How to use XeroCodes/lynx-8b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XeroCodes/lynx-8b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XeroCodes/lynx-8b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/XeroCodes/lynx-8b-gguf:F16

Ollama
How to use XeroCodes/lynx-8b-gguf with Ollama:
```
ollama run hf.co/XeroCodes/lynx-8b-gguf:F16
```

Unsloth Studio new

How to use XeroCodes/lynx-8b-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 XeroCodes/lynx-8b-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 XeroCodes/lynx-8b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XeroCodes/lynx-8b-gguf to start chatting

Docker Model Runner
How to use XeroCodes/lynx-8b-gguf with Docker Model Runner:
```
docker model run hf.co/XeroCodes/lynx-8b-gguf:F16
```

Lemonade

How to use XeroCodes/lynx-8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XeroCodes/lynx-8b-gguf:F16

Run and chat with the model

lemonade run user.lynx-8b-gguf-F16

List all available models

lemonade list

Samarvir S Vasale commited on Jul 28, 2024

Commit

e75acfb

verified ·

1 Parent(s): af049ea

Update README.md

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README.md +22 -3

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@@ -1,3 +1,22 @@
----
-license: apache-2.0
----

+---
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+datasets:
+  - sahil2801/CodeAlpaca-20k
+language:
+  - en
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+---
+## Lynx-8B
+Lynx-8B is a fine-tuned language model based on Meta's LLaMA-3.1-8B, specifically optimized to be an exceptional coding assistant. This model has been fine-tuned using the CodeAlpaca-20k dataset, which enhances its capability to understand and generate code across various programming languages and scenarios.
+## Overview
+Lynx-8B leverages the robust foundation of the Meta-Llama-3.1-8B-Instruct model and fine-tunes it with the comprehensive CodeAlpaca-20k dataset. This results in a model that excels in coding tasks, offering precise and context-aware code suggestions, completions, and explanations.
+## Features
+- Code Generation: Generate syntactically correct and efficient code snippets.
+- Code Completion: Provide context-aware code completions to accelerate your development process.
+- Code Explanation: Offer explanations for code snippets, helping you understand complex logic.
+- Multi-Language Support: Supports a wide range of programming languages including Python, JavaScript, Java, C++, and more.