Instructions to use AthenaAgent/Mockingbirdv1-merged-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AthenaAgent/Mockingbirdv1-merged-SFT with Transformers:

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

pipe = pipeline("text-generation", model="AthenaAgent/Mockingbirdv1-merged-SFT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AthenaAgent/Mockingbirdv1-merged-SFT")
model = AutoModelForCausalLM.from_pretrained("AthenaAgent/Mockingbirdv1-merged-SFT")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AthenaAgent/Mockingbirdv1-merged-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AthenaAgent/Mockingbirdv1-merged-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AthenaAgent/Mockingbirdv1-merged-SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AthenaAgent/Mockingbirdv1-merged-SFT

SGLang

How to use AthenaAgent/Mockingbirdv1-merged-SFT 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 "AthenaAgent/Mockingbirdv1-merged-SFT" \
    --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": "AthenaAgent/Mockingbirdv1-merged-SFT",
		"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 "AthenaAgent/Mockingbirdv1-merged-SFT" \
        --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": "AthenaAgent/Mockingbirdv1-merged-SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AthenaAgent/Mockingbirdv1-merged-SFT with Docker Model Runner:
```
docker model run hf.co/AthenaAgent/Mockingbirdv1-merged-SFT
```

Model Card for Model ID

Mockingbirdv1-SFT

An opinionated Large Langugae Model. Model is trained to pick a perspective on given topic and then provide nuanced arguments to back up that worldview.

Model Details

Model Description

Developed by: AthenaAgent
Language(s) (NLP): English
License: Apache-2.0
Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2

Uses

The model uses same instruction formet as Mistral Instruct. Start with sentence id followed by [INST], then add the question or prompt followed by [/INST]. For example, tokenizer.bos_token + "[INST] Is accelerating the techno-capital machine best bet for humanity's survival? [/INST]"

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

AthenaAgent
/

Mockingbirdv1-merged-SFT

Model Card for Model ID

Model Details

Model Description

Uses

Dataset used to train AthenaAgent/Mockingbirdv1-merged-SFT