Instructions to use Chat-Error/Kimiko-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chat-Error/Kimiko-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="Chat-Error/Kimiko-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chat-Error/Kimiko-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("Chat-Error/Kimiko-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chat-Error/Kimiko-Mistral-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Chat-Error/Kimiko-Mistral-7B

SGLang

How to use Chat-Error/Kimiko-Mistral-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 "Chat-Error/Kimiko-Mistral-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": "Chat-Error/Kimiko-Mistral-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 "Chat-Error/Kimiko-Mistral-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": "Chat-Error/Kimiko-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Chat-Error/Kimiko-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/Chat-Error/Kimiko-Mistral-7B
```

state of the kimiko chat format

by SlimeQ - opened Dec 20, 2023

Discussion

SlimeQ

Dec 20, 2023

I've been building a chatbot which I've standardized on an extended version of what I understand to be the Kimiko chat format. My chat format builds upon your <<AIBOT>> syntax and adds a perspective tag. So I can do things like:

<<slimeq/CHAT>> (a publicly visible text message)
<<slimeq/SPEAKING>> (an internal, narrative message spoken by a character in my story)
<<slimeq/THOUGHT>> (an internal, narrative message thought by a character in my story, which nobody else will see)

To accomplish this I've been building a dataset and training a lora. My first attempt was a fine tune of the original nous-hermes kimiko, and then I moved on to MythoMax and then Tiefighter, both of which immediately understood the Kimiko chat format and were able to adapt effectively.

Now though I'm attempting to move to a mistral base and I've found that this chat format doesn't really work at all. I'm wondering what your plans are @nRuaif , are you abandoning this chat format or planning to train it into a mistral model? It seems like a chatML conversion would be relatively easy, just curious if you've run into this problem yet

Chat-Error

Owner Dec 21, 2023

I was experimenting with stuff back then with custom format but I get many complains about it so I switched to that Vicuna 1.1.
I will think about ChatML, not really my favorite format but seems like everyone is using it.

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