Instructions to use alinaryan/merlinite-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alinaryan/merlinite-7b with Transformers:

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

pipe = pipeline("text-generation", model="alinaryan/merlinite-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alinaryan/merlinite-7b")
model = AutoModelForCausalLM.from_pretrained("alinaryan/merlinite-7b")

llama-cpp-python

How to use alinaryan/merlinite-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alinaryan/merlinite-7b",
	filename="release-040424.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 alinaryan/merlinite-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alinaryan/merlinite-7b
# Run inference directly in the terminal:
llama-cli -hf alinaryan/merlinite-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alinaryan/merlinite-7b
# Run inference directly in the terminal:
llama-cli -hf alinaryan/merlinite-7b

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 alinaryan/merlinite-7b
# Run inference directly in the terminal:
./llama-cli -hf alinaryan/merlinite-7b

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 alinaryan/merlinite-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alinaryan/merlinite-7b

Use Docker

docker model run hf.co/alinaryan/merlinite-7b

LM Studio
Jan

vLLM

How to use alinaryan/merlinite-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alinaryan/merlinite-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alinaryan/merlinite-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alinaryan/merlinite-7b

SGLang

How to use alinaryan/merlinite-7b 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 "alinaryan/merlinite-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alinaryan/merlinite-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "alinaryan/merlinite-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alinaryan/merlinite-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use alinaryan/merlinite-7b with Ollama:
```
ollama run hf.co/alinaryan/merlinite-7b
```

Unsloth Studio new

How to use alinaryan/merlinite-7b 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 alinaryan/merlinite-7b 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 alinaryan/merlinite-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alinaryan/merlinite-7b to start chatting

Docker Model Runner
How to use alinaryan/merlinite-7b with Docker Model Runner:
```
docker model run hf.co/alinaryan/merlinite-7b
```

Lemonade

How to use alinaryan/merlinite-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alinaryan/merlinite-7b

Run and chat with the model

lemonade run user.merlinite-7b-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Labrador Synthetic Data Generation Pipeline

Introduction

This repository contains the Labrador synthetic data generation pipeline, which is used to generate synthetic data for various purposes.

Run Instructions (Automation)

Step 1: Environment Setup

Initialize a .env file with the following access tokens:

GIT_ACCESS_TOKEN={ACCESS-TOKEN-TO-ACCESS-TAXONOMY-REPO} # this personal access token is used to access instruct-lab/taxonomy repo

Step 2: Execution

To run the pipeline:

Execute the following command:

NOTE: Depending on whether you are running on old or new vela, change this line in the orchestrator.py to use the appropriate old vela or new vela template. save_job_with_jinja_template(cfg, "templates/labrador_datagen_vela.yaml.j2", output_dir=f"jobs/{branch}")
```
python orchestrator.py branch-name
```
This will:
- Create a file with a list of leaf nodes in the jobs directory.
- Generate YAML files for each leaf node and store them in the jobs directory something like test-7984f9cae729b798bed1ba222715b880.yaml
To initiate the skill generation pipeline, run:

To trigger a job, take the above yaml and
```
oc create -f jobs/yaml_name.yaml
```
This command will execute the pipeline and store the results in the new_data/labrador-datagen directory within the COS bucket mounted on the Vela cluster.

Run Instructions (Manual - Testing)

Step 1: Run model

Run teacher model - this model can be replaced with any small model for testing purposes

text-generation-launcher -p 8080 --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 --dtype bfloat16 --max-input-length 4096  --max-batch-prefill-tokens 4096 --max-total-tokens 12288

Next, set the following enviornment variables:

  LEAF_NODE=knowledge/textbooks/ethics/qna.yaml # Path to the leaf node that you want to download
  NUM_SAMPLES=30
  NUM_GROUNDED_QUESTIONS=3
  NUM_GEN_PROC=32
  NUM_UTIL_PROC=8
  SAVE_PATH=new_data/labrador_datagen # Path where you want to download the data
  CONTEXT=0 # Set 0 for freeform and 1 for grounded
  DATA_PATH=.
  CHECKSUM=test
  BRANCH_NAME=test # Branch name to download data from
  KNOWLEDGE=1 # Set 0 for skills and 1 for knowledge
  PARENT_DIR=$(dirname "$LEAF_NODE")
  GIT_ACCESS_TOKEN= # Access token to access taxonomy repo

Skills

Download data

wget --header "Authorization: token $GIT_ACCESS_TOKEN" --directory-prefix="$DATA_PATH/$PARENT_DIR" "https://raw.githubusercontent.com/instruct-lab/taxonomy/$BRANCH_NAME/$LEAF_NODE"

Run the Justfile using:

just run

The Justfile will check the context value. If the context is set to 1, it will run scripts for grounded data generation. If the context is set to 0, it will run scripts for freeform data generation and save the generated files in the root of the repo in the same directory structure.

Knowledge

Download data

bash download_docs.sh

Run knowledge script

python knowledge_generation_pipeline.py

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Safetensors

Model size

7B params

Tensor type

BF16