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How to use tinyopsec/RoLlama3-8b-Instruct-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 tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
docker model run hf.co/tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
How to use tinyopsec/RoLlama3-8b-Instruct-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/RoLlama3-8b-Instruct-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": "tinyopsec/RoLlama3-8b-Instruct-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
How to use tinyopsec/RoLlama3-8b-Instruct-GGUF with Ollama:
ollama run hf.co/tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
How to use tinyopsec/RoLlama3-8b-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
How to use tinyopsec/RoLlama3-8b-Instruct-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tinyopsec/RoLlama3-8b-Instruct-GGUF:Q4_K_M
lemonade run user.RoLlama3-8b-Instruct-GGUF-Q4_K_M
lemonade list
GGUF quantizations of OpenLLM-Ro/RoLlama3-8b-Instruct, converted with llama.cpp.
RoLlama3-8b-Instruct is an instruction-tuned Romanian language model from the OpenLLM-Ro project, based on Meta Llama 3 8B. It is fine-tuned for Romanian instruction following and chat.
Features:
<|start_header_id|> / <|eot_id|>)| File | Bits | Approx. Size | Use Case |
|---|---|---|---|
| model_f16.gguf | 16 | ~16.1 GB | Full precision, reference / requantization |
| model_q8_0.gguf | 8 | ~8.5 GB | Near-lossless |
| model_q6_k.gguf | 6 | ~6.6 GB | Very high quality |
| model_q5_k_m.gguf | 5 | ~5.7 GB | High quality, recommended for quality |
| model_q5_k_s.gguf | 5 | ~5.6 GB | High quality, slightly smaller |
| model_q4_k_m.gguf | 4 | ~4.9 GB | Best balance, recommended |
| model_q4_k_s.gguf | 4 | ~4.7 GB | Balanced, smaller |
| model_q3_k_l.gguf | 3 | ~4.3 GB | Low memory, some quality loss |
| model_q3_k_m.gguf | 3 | ~4.0 GB | Low memory |
| model_q3_k_s.gguf | 3 | ~3.7 GB | Very low memory |
| model_q2_k.gguf | 2 | ~3.2 GB | Minimum size, noticeable quality loss |
| Quant | RAM / VRAM (approx., 4K context) |
|---|---|
| F16 | ~18 GB |
| Q8_0 | ~10 GB |
| Q6_K | ~8 GB |
| Q5_K_M / Q5_K_S | ~7 GB |
| Q4_K_M / Q4_K_S | ~6 GB |
| Q3_K_L / Q3_K_M / Q3_K_S | ~5-6 GB |
| Q2_K | ~4.5 GB |
./llama-cli -m model_q4_k_m.gguf -p "Salut! Cum te poΘi ajuta?" -n 256 -c 4096
Server mode:
./llama-server -m model_q4_k_m.gguf -c 4096 --port 8080
from llama_cpp import Llama
llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=4096)
out = llm.create_chat_completion(
messages=[{"role": "user", "content": "Salut! Cum te poΘi ajuta?"}]
)
print(out["choices"][0]["message"]["content"])
.gguf file from this repository.<publisher>/<model>/), or use the in-app import.huggingface-cli download tinyopsec/RoLlama3-8b-Instruct-GGUF model_q4_k_m.gguf --local-dir .
cat > Modelfile <<EOF
FROM ./model_q4_k_m.gguf
EOF
ollama create rollama3-8b-instruct -f Modelfile
ollama run rollama3-8b-instruct
<|eom_id|> token (id 128256) was removed from the tokenizer before conversion to match the model's embedding size (128256).llama-quantize from the llama.cpp project.4-bit
5-bit
6-bit
8-bit
16-bit
Base model
meta-llama/Meta-Llama-3-8B-Instruct