Instructions to use Prazzwal07/quantized_human_assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Prazzwal07/quantized_human_assistant with Transformers:

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

pipe = pipeline("text-generation", model="Prazzwal07/quantized_human_assistant")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Prazzwal07/quantized_human_assistant")
model = AutoModelForCausalLM.from_pretrained("Prazzwal07/quantized_human_assistant", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Prazzwal07/quantized_human_assistant 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 Prazzwal07/quantized_human_assistant:F16
# Run inference directly in the terminal:
llama cli -hf Prazzwal07/quantized_human_assistant:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Prazzwal07/quantized_human_assistant:F16
# Run inference directly in the terminal:
llama cli -hf Prazzwal07/quantized_human_assistant: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 Prazzwal07/quantized_human_assistant:F16
# Run inference directly in the terminal:
./llama-cli -hf Prazzwal07/quantized_human_assistant: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 Prazzwal07/quantized_human_assistant:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Prazzwal07/quantized_human_assistant:F16

Use Docker

docker model run hf.co/Prazzwal07/quantized_human_assistant:F16

LM Studio
Jan

vLLM

How to use Prazzwal07/quantized_human_assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Prazzwal07/quantized_human_assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prazzwal07/quantized_human_assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Prazzwal07/quantized_human_assistant:F16

SGLang

How to use Prazzwal07/quantized_human_assistant 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 "Prazzwal07/quantized_human_assistant" \
    --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": "Prazzwal07/quantized_human_assistant",
		"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 "Prazzwal07/quantized_human_assistant" \
        --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": "Prazzwal07/quantized_human_assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Prazzwal07/quantized_human_assistant with Ollama:
```
ollama run hf.co/Prazzwal07/quantized_human_assistant:F16
```

Unsloth Studio

How to use Prazzwal07/quantized_human_assistant 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 Prazzwal07/quantized_human_assistant 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 Prazzwal07/quantized_human_assistant to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Prazzwal07/quantized_human_assistant to start chatting

Atomic Chat new
Docker Model Runner
How to use Prazzwal07/quantized_human_assistant with Docker Model Runner:
```
docker model run hf.co/Prazzwal07/quantized_human_assistant:F16
```

Lemonade

How to use Prazzwal07/quantized_human_assistant with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Prazzwal07/quantized_human_assistant:F16

Run and chat with the model

lemonade run user.quantized_human_assistant-F16

List all available models

lemonade list

quantized_human_assistant / config.json

Prazzwal07

Create config.json

d4cc465 verified over 2 years ago

Raw

History Blame Contribute Delete

820 Bytes

	{
	"_name_or_path": "gpt2",
	"activation_function": "gelu_new",
	"architectures": [
	"GPT2LMHeadModel"
	],
	"attn_pdrop": 0.1,
	"bos_token_id": 50256,
	"embd_pdrop": 0.1,
	"eos_token_id": 50256,
	"initializer_range": 0.02,
	"layer_norm_epsilon": 0.00001,
	"model_type": "gpt2",
	"n_ctx": 1024,
	"n_embd": 768,
	"n_head": 12,
	"n_inner": null,
	"n_layer": 12,
	"n_positions": 1024,
	"reorder_and_upcast_attn": false,
	"resid_pdrop": 0.1,
	"scale_attn_by_inverse_layer_idx": false,
	"scale_attn_weights": true,
	"summary_activation": null,
	"summary_first_dropout": 0.1,
	"summary_proj_to_labels": true,
	"summary_type": "cls_index",
	"summary_use_proj": true,
	"task_specific_params": {
	"text-generation": {
	"do_sample": true,
	"max_length": 50
	}
	},
	"torch_dtype": "float32",
	"transformers_version": "4.25.1",
	"use_cache": true,
	"vocab_size": 50257
	}