Not-For-All-Audiences

Instructions to use Envoid/Fish-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Envoid/Fish-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="Envoid/Fish-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Envoid/Fish-8x7B")
model = AutoModelForCausalLM.from_pretrained("Envoid/Fish-8x7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Envoid/Fish-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Envoid/Fish-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/Fish-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Envoid/Fish-8x7B

SGLang

How to use Envoid/Fish-8x7B 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 "Envoid/Fish-8x7B" \
    --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": "Envoid/Fish-8x7B",
		"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 "Envoid/Fish-8x7B" \
        --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": "Envoid/Fish-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Envoid/Fish-8x7B with Docker Model Runner:
```
docker model run hf.co/Envoid/Fish-8x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Caution: This model may output adult content.

FISH - Fusion of Intuitive Semantic Heuristics

For this model I took the following LoRAs

SeanWu25/Mixtral_8x7b_Medicine

SeanWu25/Mixtral_8x7b_WuKurtz

and

wandb/Mixtral-8x7b-Remixtral

And merged them onto their base model.

I then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating Envoid/BondBurger-8x7B

The end results are a surprisingly good model for role palying style entertainment.

At first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life.

It does eccentuate characters that have particularly aggressive personalities.

Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness.

This model has only been tested in Q8 GGUF form due to hardware limitations.

It responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)

Downloads last month: 76

Safetensors

Model size

47B params

Tensor type

F16

Model tree for Envoid/Fish-8x7B

Quantizations

3 models