Instructions to use Sansar-Karki/BasicQueries with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sansar-Karki/BasicQueries with Transformers:

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

pipe = pipeline("text-generation", model="Sansar-Karki/BasicQueries")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sansar-Karki/BasicQueries", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sansar-Karki/BasicQueries 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 Sansar-Karki/BasicQueries:Q8_0
# Run inference directly in the terminal:
llama cli -hf Sansar-Karki/BasicQueries:Q8_0

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/Sansar-Karki/BasicQueries:Q8_0

LM Studio
Jan

vLLM

How to use Sansar-Karki/BasicQueries with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Sansar-Karki/BasicQueries:Q8_0

SGLang

How to use Sansar-Karki/BasicQueries 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 "Sansar-Karki/BasicQueries" \
    --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": "Sansar-Karki/BasicQueries",
		"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 "Sansar-Karki/BasicQueries" \
        --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": "Sansar-Karki/BasicQueries",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Sansar-Karki/BasicQueries with Ollama:
```
ollama run hf.co/Sansar-Karki/BasicQueries:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use Sansar-Karki/BasicQueries with Docker Model Runner:
```
docker model run hf.co/Sansar-Karki/BasicQueries:Q8_0
```

Lemonade

How to use Sansar-Karki/BasicQueries with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sansar-Karki/BasicQueries:Q8_0

Run and chat with the model

lemonade run user.BasicQueries-Q8_0

List all available models

lemonade list

Atomic Chat

BasicQueries / README.md

Sansar-Karki

Upload README.md with huggingface_hub

72172c7 verified about 2 years ago

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metadata

base_model: Arjun-G-Ravi/chat-GPT2
datasets:
  - MuskumPillerum/General-Knowledge
language:
  - en
library_name: transformers
license: mit
metrics:
  - accuracy
pipeline_tag: text-generation
tags:
  - chemistry
  - biology
  - text-generation-inference
  - llama-cpp
  - gguf-my-repo
widget:
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: Who is the king of the jungle? Answer:
    example_title: Knowledge - Animal kingdom
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: Who is Kobe Bryant? Answer:
    example_title: Knowledge - Sports
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: What is the meaning of life? Answer:
    example_title: Philosophy
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: What role of actuators in robotics? Answer:
    example_title: Robotics
inference:
  parameters:
    temperature: 0.7
    top_k: 50
    top_p: 0.9
    max_length: 200

Sansar-Karki/chat-GPT2-Q8_0-GGUF

This model was converted to GGUF format from Arjun-G-Ravi/chat-GPT2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -c 2048