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---
license: cc-by-nc-4.0
base_model: google/gemma-2b
model-index:
- name: Octopus-V2-2B
results: []
tags:
- function calling
- on-device language model
- android
inference: false
space: false
spaces: false
language:
- en
---
# Quantized Octopus V2: On-device language model for super agent
This repo includes two types of quantized models: **GGUF** and **AWQ**, for our Octopus V2 model at [NexaAIDev/Octopus-v2](https://huggingface.co/NexaAIDev/Octopus-v2)
<p align="center" width="100%">
<a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# GGUF Qauntization
To run the models, please download them to your local machine using either git clone or [Hugging Face Hub](https://huggingface.co/docs/huggingface_hub/en/guides/download)
```
git clone https://huggingface.co/NexaAIDev/Octopus-v2-gguf-awq
```
## Run with [llama.cpp](https://github.com/ggerganov/llama.cpp) (Recommended)
1. **Clone and compile:**
```bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
# Compile the source code:
make
```
2. **Execute the Model:**
Run the following command in the terminal:
```bash
./main -m ./path/to/octopus-v2-Q4_K_M.gguf -n 256 -p "Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: Take a selfie for me with front camera\n\nResponse:"
```
## Run with [Ollama](https://github.com/ollama/ollama)
Since our models have not been uploaded to the Ollama server, please download the models and manually import them into Ollama by following these steps:
1. Install Ollama on your local machine. You can also following the guide from [Ollama GitHub repository](https://github.com/ollama/ollama/blob/main/docs/import.md)
```bash
git clone https://github.com/ollama/ollama.git ollama
```
2. Locate the local Ollama directory:
```bash
cd ollama
```
3. Create a `Modelfile` in your directory
```bash
touch Modelfile
```
4. In the Modelfile, include a `FROM` statement with the path to your local model, and the default parameters:
```bash
FROM ./path/to/octopus-v2-Q4_K_M.gguf
PARAMETER temperature 0
PARAMETER num_ctx 1024
PARAMETER stop <nexa_end>
```
5. Use the following command to add the model to Ollama:
```bash
ollama create octopus-v2-Q4_K_M -f Modelfile
```
6. Verify that the model has been successfully imported:
```bash
ollama ls
```
7. Run the mode
```bash
ollama run octopus-v2-Q4_K_M "Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: Take a selfie for me with front camera\n\nResponse:"
```
# AWQ Quantization
Python example:
```python
from transformers import AutoTokenizer
from awq import AutoAWQForCausalLM
import torch
import time
import numpy as np
def inference(input_text):
start_time = time.time()
input_ids = tokenizer(input_text, return_tensors="pt").to('cuda')
input_length = input_ids["input_ids"].shape[1]
generation_output = model.generate(
input_ids["input_ids"],
do_sample=False,
max_length=1024
)
end_time = time.time()
# Decode only the generated part
generated_sequence = generation_output[:, input_length:].tolist()
res = tokenizer.decode(generated_sequence[0])
latency = end_time - start_time
num_output_tokens = len(generated_sequence[0])
throughput = num_output_tokens / latency
return {"output": res, "latency": latency, "throughput": throughput}
# Initialize tokenizer and model
model_id = "/path/to/Octopus-v2-AWQ-NexaAIDev"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=False)
model = AutoAWQForCausalLM.from_quantized(model_id, fuse_layers=True,
trust_remote_code=False, safetensors=True)
prompts = ["Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: Can you take a photo using the back camera and save it to the default location? \n\nResponse:"]
avg_throughput = []
for prompt in prompts:
out = inference(prompt)
avg_throughput.append(out["throughput"])
print("nexa model result:\n", out["output"])
print("avg throughput:", np.mean(avg_throughput))
```
# Quantized GGUF & AWQ Models Benchmark
| Name | Quant method | Bits | Size | Response (t/s) | Use Cases |
| ---------------------- | ------------ | ---- | -------- | -------------- | ----------------------------------- |
| Octopus-v2-AWQ | AWQ | 4 | 3.00 GB | 63.83 | fast, high quality, recommended |
| Octopus-v2-Q2_K.gguf | Q2_K | 2 | 1.16 GB | 57.81 | fast but high loss, not recommended |
| Octopus-v2-Q3_K.gguf | Q3_K | 3 | 1.38 GB | 57.81 | extremely not recommended |
| Octopus-v2-Q3_K_S.gguf | Q3_K_S | 3 | 1.19 GB | 52.13 | extremely not recommended |
| Octopus-v2-Q3_K_M.gguf | Q3_K_M | 3 | 1.38 GB | 58.67 | moderate loss, not very recommended |
| Octopus-v2-Q3_K_L.gguf | Q3_K_L | 3 | 1.47 GB | 56.92 | not very recommended |
| Octopus-v2-Q4_0.gguf | Q4_0 | 4 | 1.55 GB | 68.80 | moderate speed, recommended |
| Octopus-v2-Q4_1.gguf | Q4_1 | 4 | 1.68 GB | 68.09 | moderate speed, recommended |
| Octopus-v2-Q4_K.gguf | Q4_K | 4 | 1.63 GB | 64.70 | moderate speed, recommended |
| Octopus-v2-Q4_K_S.gguf | Q4_K_S | 4 | 1.56 GB | 62.16 | fast and accurate, very recommended |
| Octopus-v2-Q4_K_M.gguf | Q4_K_M | 4 | 1.63 GB | 64.74 | fast, recommended |
| Octopus-v2-Q5_0.gguf | Q5_0 | 5 | 1.80 GB | 64.80 | fast, recommended |
| Octopus-v2-Q5_1.gguf | Q5_1 | 5 | 1.92 GB | 63.42 | very big, prefer Q4 |
| Octopus-v2-Q5_K.gguf | Q5_K | 5 | 1.84 GB | 61.28 | big, recommended |
| Octopus-v2-Q5_K_S.gguf | Q5_K_S | 5 | 1.80 GB | 62.16 | big, recommended |
| Octopus-v2-Q5_K_M.gguf | Q5_K_M | 5 | 1.71 GB | 61.54 | big, recommended |
| Octopus-v2-Q6_K.gguf | Q6_K | 6 | 2.06 GB | 55.94 | very big, not very recommended |
| Octopus-v2-Q8_0.gguf | Q8_0 | 8 | 2.67 GB | 56.35 | very big, not very recommended |
| Octopus-v2-f16.gguf | f16 | 16 | 5.02 GB | 36.27 | extremely big |
| Octopus-v2.gguf | | | 10.00 GB | | |
_Quantized with llama.cpp_
**Acknowledgement**:
We sincerely thank our community members, [Mingyuan](https://huggingface.co/ThunderBeee), [Zoey](https://huggingface.co/ZY6), [Brian](https://huggingface.co/JoyboyBrian), [Perry](https://huggingface.co/PerryCheng614), [Qi](https://huggingface.co/qiqiWav), [David](https://huggingface.co/Davidqian123) for their extraordinary contributions to this quantization effort. |