Instructions to use SIRIS-Lab/impuls-salamandra-7b-query-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SIRIS-Lab/impuls-salamandra-7b-query-parser with Transformers:

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

pipe = pipeline("text-generation", model="SIRIS-Lab/impuls-salamandra-7b-query-parser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SIRIS-Lab/impuls-salamandra-7b-query-parser")
model = AutoModelForCausalLM.from_pretrained("SIRIS-Lab/impuls-salamandra-7b-query-parser", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SIRIS-Lab/impuls-salamandra-7b-query-parser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SIRIS-Lab/impuls-salamandra-7b-query-parser

SGLang

How to use SIRIS-Lab/impuls-salamandra-7b-query-parser 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 "SIRIS-Lab/impuls-salamandra-7b-query-parser" \
    --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": "SIRIS-Lab/impuls-salamandra-7b-query-parser",
		"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 "SIRIS-Lab/impuls-salamandra-7b-query-parser" \
        --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": "SIRIS-Lab/impuls-salamandra-7b-query-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SIRIS-Lab/impuls-salamandra-7b-query-parser with Docker Model Runner:
```
docker model run hf.co/SIRIS-Lab/impuls-salamandra-7b-query-parser
```

impuls-salamandra-7b-query-parser

File size: 520 Bytes

91261b2

{
  "model": "langtech-innovation/7b-tools-v3",
  "data": "data/training/impulse_training.jsonl",
  "output_dir": "models/impulse-7b-tools-v3-ft",
  "epochs": 5,
  "batch_size": 1,
  "gradient_accumulation_steps": 16,
  "learning_rate": 0.0001,
  "max_length": 4096,
  "quantize": "4bit",
  "warmup_steps": 100,
  "lora_r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "eval_split": 0.1,
  "seed": 42,
  "use_wandb": false,
  "early_stopping_patience": 3,
  "eval_accumulation_steps": 4,
  "skip_perplexity": true
}