Instructions to use jrosell/salamandra-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jrosell/salamandra-2b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="jrosell/salamandra-2b-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jrosell/salamandra-2b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jrosell/salamandra-2b-GGUF 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 jrosell/salamandra-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jrosell/salamandra-2b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jrosell/salamandra-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jrosell/salamandra-2b-GGUF:Q4_K_M

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 jrosell/salamandra-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jrosell/salamandra-2b-GGUF:Q4_K_M

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 jrosell/salamandra-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jrosell/salamandra-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jrosell/salamandra-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use jrosell/salamandra-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jrosell/salamandra-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jrosell/salamandra-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jrosell/salamandra-2b-GGUF:Q4_K_M

SGLang

How to use jrosell/salamandra-2b-GGUF 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 "jrosell/salamandra-2b-GGUF" \
    --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": "jrosell/salamandra-2b-GGUF",
		"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 "jrosell/salamandra-2b-GGUF" \
        --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": "jrosell/salamandra-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use jrosell/salamandra-2b-GGUF with Ollama:
```
ollama run hf.co/jrosell/salamandra-2b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use jrosell/salamandra-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/jrosell/salamandra-2b-GGUF:Q4_K_M
```

Lemonade

How to use jrosell/salamandra-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jrosell/salamandra-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.salamandra-2b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

In this repo you can find the quantized versions of "BSC-LT/salamandra-2b" ready to use in ollama and R.

Installation

To use it, first download this repo:

$ hf download "jrosell/salamandra-2b-GGUF"
$ cd "$HOME/.cache/huggingface/hub/models--BSC-LT--salamandra-2b/snapshots/$(cat ~/.cache/huggingface/hub/models--BSC-LT--salamandra-2b/refs/main)"

Usage

Use GGUF Q8_0 version in Ollama

$ echo "FROM ./models/salamandra-2b-Q8_0.gguf" > Modelfile.Q8_0
$ ollama create salamandra-2b:Q8_0 -f Modelfile.Q8_0
$ ollama run salamandra-2b:Q8_0

Use the Quantized Q4_K_M version in Ollama:

$ echo "FROM ./models/salamandra-2b-Q4_K_M.gguf" > Modelfile.Q4_K_M
$ ollama create salamandra-2b:Q4_K_M -f Modelfile.Q4_K_M
$ ollama run salamandra-2b:Q4_K_M

Use it in R:

$ R --vanilla -e 'ellmer::chat_ollama(model = "salamandra-2b:Q4_K_M")$chat("Explica un acudit sobre informàtics.")'

How I built this

$ mkdir -p salamandra-2b/models && cd salamandra-2b
$ hf download "BSC-LT/salamandra-2b"
$ git clone https://github.com/ggerganov/llama.cpp.git
$ uv init
$ uv add -r llama.cpp/requirements.txt
$ export MODEL_DIR="$HOME/.cache/huggingface/hub/models--BSC-LT--salamandra-2b/snapshots/$(cat ~/.cache/huggingface/hub/models--BSC-LT--salamandra-2b/refs/main)"
$ uv run llama.cpp/convert_hf_to_gguf.py $MODEL_DIR \
    --outfile models/salamandra-2b.gguf \
    --outtype auto
$ cd llama.cpp
$ cmake -B build
$ cmake --build build --config Release --target llama-quantize
$ ./build/bin/llama-quantize ../models/salamandra-2b.gguf ../models/salamandra-2b-Q4_K_M.gguf Q4_K_M

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GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for jrosell/salamandra-2b-GGUF

Base model

BSC-LT/salamandra-2b

Quantized

(6)

this model

jrosell
/

salamandra-2b-GGUF

Installation

Usage

How I built this

Model tree for jrosell/salamandra-2b-GGUF

Datasets used to train jrosell/salamandra-2b-GGUF