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Model: BSC-LT/salamandra-7b-instruct-tools-16k
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---
metrics:
format: gguf
method: gguf
quantization_type: Q4_K_M
context_length: 2048
tags:
- quantization
name: GGUF
description: GGUF quantization using llama.cpp for efficient CPU and GPU inference
intended_use: Efficient inference on CPU and GPU with llama.cpp
limitations: Requires llama.cpp conversion tools and specific model architectures
citations:
- https://github.com/ggml-org/llama.cpp
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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### Direct Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
> [!WARNING]
> **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.
>
### 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))
```
#### Output:
```text
<tool_call>
{"name": "get_weather", "arguments": {"location": "Paris, France"}}
</tool_call>
```
### 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,
)
msg = chat_completion.choices[0].message
# --- HANDLE TOOL CALL OR NORMAL CONTENT ---
if not getattr(msg, "tool_calls", None):
# Normal assistant message
print(msg.content)
messages.append({
"role": "assistant",
"content": msg.content
})
else:
# Assistant tool call message
print(msg.tool_calls)
messages.append({"role": "assistant", "tool_calls": msg.tool_calls})
# --- Fake tool execution example ---
tool_call = msg.tool_calls[0]
# Example: handle the get_weather tool
if tool_call.function.name == "get_weather":
# Fake tool result (this would come from your actual backend)
fake_tool_result = '{"temperature": 18, "unit": "C", "description": "Partly cloudy in Paris"}'
# Append the tool result message so the model can use it in the next turn
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call.function.name,
"content": fake_tool_result,
})
```

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"</tool_response>": 256004,
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"<tools>": 256001
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{
"_name_or_path": "/gpfs/projects/bsc88/hf-models/Salamandra-7b_pre-1.3-160k_sft-2.0_openlicenses_ankush",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
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"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 16384,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
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"rope_type": "llama3"
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"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.44.0",
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"vocab_size": 256006
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