Instructions to use nakodanei/Red-Daffodil-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nakodanei/Red-Daffodil-7B with Transformers:

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

pipe = pipeline("text-generation", model="nakodanei/Red-Daffodil-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nakodanei/Red-Daffodil-7B")
model = AutoModelForCausalLM.from_pretrained("nakodanei/Red-Daffodil-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nakodanei/Red-Daffodil-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nakodanei/Red-Daffodil-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nakodanei/Red-Daffodil-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nakodanei/Red-Daffodil-7B

SGLang

How to use nakodanei/Red-Daffodil-7B 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 "nakodanei/Red-Daffodil-7B" \
    --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": "nakodanei/Red-Daffodil-7B",
		"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 "nakodanei/Red-Daffodil-7B" \
        --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": "nakodanei/Red-Daffodil-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nakodanei/Red-Daffodil-7B with Docker Model Runner:
```
docker model run hf.co/nakodanei/Red-Daffodil-7B
```

Red-Daffodil-7B

Roleplaying focused model based on Mistral-7b-v0.1. Aims to produce natural human-like text, free from slop.

Uses a model based on aselection of LoRA trained on almost entirely human text, which is then merged with opus-v0-7b and AdventurousWinds-Mk2-7b. The resulting model is merged together with Noromaid-7b-v0.2 back into the Mistral base model with LM Cocktail to maintain generalisation and intelligence.

Prompt template:

### Input:
User: {prompt}

### Response:
Character:

Alpaca prompt template should work fine too.

opus-v0-7b: https://huggingface.co/dreamgen/opus-v0-7b

AdventurousWinds-Mk2-7b https://huggingface.co/PocketDoc/Dans-AdventurousWinds-Mk2-7b

Noromaid: https://huggingface.co/NeverSleep/Noromaid-7b-v0.2/

LM Cocktail: https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

F16