Instructions to use N8Programs/Coxcomb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N8Programs/Coxcomb with Transformers:

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

pipe = pipeline("text-generation", model="N8Programs/Coxcomb")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("N8Programs/Coxcomb")
model = AutoModelForCausalLM.from_pretrained("N8Programs/Coxcomb")
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

vLLM

How to use N8Programs/Coxcomb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N8Programs/Coxcomb"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N8Programs/Coxcomb",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N8Programs/Coxcomb

SGLang

How to use N8Programs/Coxcomb 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 "N8Programs/Coxcomb" \
    --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": "N8Programs/Coxcomb",
		"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 "N8Programs/Coxcomb" \
        --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": "N8Programs/Coxcomb",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use N8Programs/Coxcomb with Docker Model Runner:
```
docker model run hf.co/N8Programs/Coxcomb
```

N8Programs commited on Apr 17, 2024

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+---
+license: apache-2.0
+datasets:
+- N8Programs/CreativeGPT
+language:
+- en
+pipeline_tag: text-generation
+---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647e98971a1fcad2fdc55e61/Fv6NrdPI0U6AZmnafez3T.png)
+# Model Card for Coxcomb
+A creative writing model, using the superb [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2) as a base, finetuned on GPT-4 outputs to a diverse variety of prompts. It in no way competes with GPT-4 - it's quality of writing is below it, and it is primarily meant to be run in offline, local environments.
+On creative writing benchmarks, it is consistently ranked higher than most other models - [it scores 72.37](https://eqbench.com/creative_writing.html), beating goliath-120b, yi chat, and mistral-large.
+It is designed for **single-shot interactions**. You ask it to write a story, and it does. It is NOT designed for chat purposes, roleplay, or follow-up questions.
+## Model Details
+Trained w/ a 40M parameter lora on [N8Programs/CreativeGPT](https://huggingface.co/datasets/N8Programs/CreativeGPT) for 3 epochs. Overfit slightly (for much better benchmark results).
+### Model Description
+- **Developed by:** N8Programs
+- **Model type:** Mistral
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
+## Uses
+Bot trained on NSFW (sexual or violent) content but will generate it when asked - it has not been trained with refusals. If you wish to ADD refusal behavior in, further tuning or filtering will be neccessary.
+### Direct Use
+GGUFs available at [Coxcomb-GGUF](https://huggingface.co/N8Programs/Coxcomb-GGUF)
+Should work with transformers (not officially tested).
+## Bias, Risks, and Limitations
+Tends to generate stories with happy, trite endings. Most LLMs do this. It's very hard to get them not to.
+## Training Details
+Trained on a single M3 Max in roughly 12 hours.