Instructions to use BSC-LT/salamandra-7b-instruct-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BSC-LT/salamandra-7b-instruct-tools with Transformers:

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

pipe = pipeline("text-generation", model="BSC-LT/salamandra-7b-instruct-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BSC-LT/salamandra-7b-instruct-tools")
model = AutoModelForCausalLM.from_pretrained("BSC-LT/salamandra-7b-instruct-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use BSC-LT/salamandra-7b-instruct-tools with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BSC-LT/salamandra-7b-instruct-tools",
	filename="model-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use BSC-LT/salamandra-7b-instruct-tools with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0
# Run inference directly in the terminal:
llama-cli -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0
# Run inference directly in the terminal:
llama-cli -hf BSC-LT/salamandra-7b-instruct-tools: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 BSC-LT/salamandra-7b-instruct-tools:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf BSC-LT/salamandra-7b-instruct-tools: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 BSC-LT/salamandra-7b-instruct-tools:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0

Use Docker

docker model run hf.co/BSC-LT/salamandra-7b-instruct-tools:Q8_0

LM Studio
Jan

vLLM

How to use BSC-LT/salamandra-7b-instruct-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BSC-LT/salamandra-7b-instruct-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BSC-LT/salamandra-7b-instruct-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BSC-LT/salamandra-7b-instruct-tools:Q8_0

SGLang

How to use BSC-LT/salamandra-7b-instruct-tools 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 "BSC-LT/salamandra-7b-instruct-tools" \
    --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": "BSC-LT/salamandra-7b-instruct-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BSC-LT/salamandra-7b-instruct-tools" \
        --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": "BSC-LT/salamandra-7b-instruct-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use BSC-LT/salamandra-7b-instruct-tools with Ollama:
```
ollama run hf.co/BSC-LT/salamandra-7b-instruct-tools:Q8_0
```

Unsloth Studio new

How to use BSC-LT/salamandra-7b-instruct-tools 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 BSC-LT/salamandra-7b-instruct-tools 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 BSC-LT/salamandra-7b-instruct-tools to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BSC-LT/salamandra-7b-instruct-tools to start chatting

Pi new

How to use BSC-LT/salamandra-7b-instruct-tools with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "BSC-LT/salamandra-7b-instruct-tools:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BSC-LT/salamandra-7b-instruct-tools with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BSC-LT/salamandra-7b-instruct-tools:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default BSC-LT/salamandra-7b-instruct-tools:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use BSC-LT/salamandra-7b-instruct-tools with Docker Model Runner:
```
docker model run hf.co/BSC-LT/salamandra-7b-instruct-tools:Q8_0
```

Lemonade

How to use BSC-LT/salamandra-7b-instruct-tools with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BSC-LT/salamandra-7b-instruct-tools:Q8_0

Run and chat with the model

lemonade run user.salamandra-7b-instruct-tools-Q8_0

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

WARNING: This is a language model that has undergone instruction tuning for conversational settings that exploit function calling capabilities. It has not been aligned with human preferences. As a result, it may generate outputs that are inappropriate, misleading, biased, or unsafe. These risks can be mitigated through additional post-training stages, which is strongly recommended before deployment in any production system, especially for high-stakes applications.
NOTE: This is a GATED model, intended only for internal and external tests. Do not request access if you have not already contact us and have been given permission to test it. Please write carlos.rodriguez1(at)bsc.es to justify use, and we can grant access.

How to use

from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "BSC-LT/salamandra-7b-instruct"

text = "What is the weather like in Paris today?"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
  )

message = [ { "role": "user", "content": text } ]

tools = [{
    "type": "function",
    "name": "get_weather",
    "description": "Get current temperature for a given location.",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City and country e.g. Bogotá, Colombia"
            }
        },
        "required": [
            "location"
        ],
        "additionalProperties": False
    }
}]


prompt = tokenizer.apply_chat_template(
    message,
    tokenize=False,
    add_generation_prompt=True,
    tools=tools
)

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=1000)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Deploy with vllm

Deploy the model using vllm docker image.

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    -p 80:80 \
    vllm/vllm-openai:latest \
    --model BSC-LT/salamandra-7b-instruct-tools \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --max_model_len 8196 \
    --port 80

Then use it with openai api

pip install openai

from openai import OpenAI
client = OpenAI(
        base_url="http://localhost:8080/v1/", 
        api_key="hf_xxxx"
    )

models = client.models.list()
model = models.data[0].id

system_message = ""
messages = [{ "role": "system", "content": system_message}] if system_message else []
messages.append( {"role":"user", "content": "What is the weather like in Paris today?"})
print(messages)
chat_completion = client.chat.completions.create(
    model=model,
    tools=tools
    messages=messages,
    stream=False,
    max_tokens=1000,
    temperature=0.1,
    frequency_penalty=0.2,
)

print(chat_completion)

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Model size

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Tensor type

BF16

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