HuggingFaceH4/ultrachat_200k
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How to use second-state/stablelm-2-zephyr-1.6b-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="second-state/stablelm-2-zephyr-1.6b-GGUF") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("second-state/stablelm-2-zephyr-1.6b-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/stablelm-2-zephyr-1.6b-GGUF", device_map="auto")How to use second-state/stablelm-2-zephyr-1.6b-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
# 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 second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
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 second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
docker model run hf.co/second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
How to use second-state/stablelm-2-zephyr-1.6b-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/stablelm-2-zephyr-1.6b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "second-state/stablelm-2-zephyr-1.6b-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
How to use second-state/stablelm-2-zephyr-1.6b-GGUF with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "second-state/stablelm-2-zephyr-1.6b-GGUF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "second-state/stablelm-2-zephyr-1.6b-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "second-state/stablelm-2-zephyr-1.6b-GGUF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "second-state/stablelm-2-zephyr-1.6b-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use second-state/stablelm-2-zephyr-1.6b-GGUF with Ollama:
ollama run hf.co/second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
How to use second-state/stablelm-2-zephyr-1.6b-GGUF with Docker Model Runner:
docker model run hf.co/second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
How to use second-state/stablelm-2-zephyr-1.6b-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull second-state/stablelm-2-zephyr-1.6b-GGUF:Q4_K_M
lemonade run user.stablelm-2-zephyr-1.6b-GGUF-Q4_K_M
lemonade list
stabilityai/stablelm-2-zephyr-1_6b
LlamaEdge version: v0.2.9 and above
Prompt template
Prompt type: stablelm-zephyr
Prompt string
<|user|>
{prompt}<|endoftext|>
<|assistant|>
Reverse prompt: <|endoftext|>
Context size: 2048
Run as LlamaEdge service
wasmedge --dir .:. --nn-preload default:GGML:AUTO:stablelm-2-zephyr-1_6b-Q5_K_M.gguf llama-api-server.wasm -p stablelm-zephyr -r '<|endoftext|>' -c 1024
Run as LlamaEdge command app
wasmedge --dir .:. --nn-preload default:GGML:AUTO:stablelm-2-zephyr-1_6b-Q5_K_M.gguf llama-chat.wasm -p stablelm-zephyr -r '<|endoftext|>' --temp 0.5 -c 1024
| Name | Quant method | Bits | Size | Use case |
|---|---|---|---|---|
| stablelm-2-zephyr-1_6b-Q2_K.gguf | Q2_K | 2 | 694 MB | smallest, significant quality loss - not recommended for most purposes |
| stablelm-2-zephyr-1_6b-Q3_K_L.gguf | Q3_K_L | 3 | 915 MB | small, substantial quality loss |
| stablelm-2-zephyr-1_6b-Q3_K_M.gguf | Q3_K_M | 3 | 858 MB | very small, high quality loss |
| stablelm-2-zephyr-1_6b-Q3_K_S.gguf | Q3_K_S | 3 | 792 MB | very small, high quality loss |
| stablelm-2-zephyr-1_6b-Q4_0.gguf | Q4_0 | 4 | 983 MB | legacy; small, very high quality loss - prefer using Q3_K_M |
| stablelm-2-zephyr-1_6b-Q4_K_M.gguf | Q4_K_M | 4 | 1.03 GB | medium, balanced quality - recommended |
| stablelm-2-zephyr-1_6b-Q4_K_S.gguf | Q4_K_S | 4 | 989 MB | small, greater quality loss |
| stablelm-2-zephyr-1_6b-Q5_0.gguf | Q5_0 | 5 | 1.16 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| stablelm-2-zephyr-1_6b-Q5_K_M.gguf | Q5_K_M | 5 | 1.19 GB | large, very low quality loss - recommended |
| stablelm-2-zephyr-1_6b-Q5_K_S.gguf | Q5_K_S | 5 | 1.16 GB | large, low quality loss - recommended |
| stablelm-2-zephyr-1_6b-Q6_K.gguf | Q6_K | 6 | 1.35 GB | very large, extremely low quality loss |
| stablelm-2-zephyr-1_6b-Q8_0.gguf | Q8_0 | 8 | 1.75 GB | very large, extremely low quality loss - not recommended |
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