Instructions to use Chickaboo/fine-mixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chickaboo/fine-mixtral with Transformers:

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

pipe = pipeline("text-generation", model="Chickaboo/fine-mixtral")
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("Chickaboo/fine-mixtral")
model = AutoModelForCausalLM.from_pretrained("Chickaboo/fine-mixtral", 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 Chickaboo/fine-mixtral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Chickaboo/fine-mixtral

SGLang

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

Docker Model Runner
How to use Chickaboo/fine-mixtral with Docker Model Runner:
```
docker model run hf.co/Chickaboo/fine-mixtral
```

fine-mixtral / README.md

Chickaboo

Update README.md

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	---
	license: mit
	---

	### Model Description

	This model is a mixture of experts merge consisting of 3 mistral based models:

	base model, openchat/openchat-3.5-0106

	code expert, beowolx/CodeNinja-1.0-OpenChat-7B

	math expert, meta-math/MetaMath-Mistral-7B

	This is the config used in the merging process:
	``` yaml
	base_model: openchat/openchat-3.5-0106
	experts:
	- source_model: openchat/openchat-3.5-0106
	positive_prompts:
	- "chat"
	- "assistant"
	- "tell me"
	- "explain"
	- "I want"
	- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
	positive_prompts:
	- "code"
	- "python"
	- "javascript"
	- "programming"
	- "algorithm"
	- "C#"
	- "C++"
	- "debug"
	- "runtime"
	- "html"
	- "command"
	- "nodejs"
	- source_model: meta-math/MetaMath-Mistral-7B
	positive_prompts:
	- "reason"
	- "math"
	- "mathematics"
	- "solve"
	- "count"
	- "calculate"
	- "arithmetic"
	- "algebra"
	```
	### Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	device = "cuda" # the device to load the model onto

	model = AutoModelForCausalLM.from_pretrained("Chickaboo/Chicka-Mistral-4x7b")
	tokenizer = AutoTokenizer.from_pretrained("Chickaboo/Chicka-Mistral-4x7b")

	messages = [
	{"role": "user", "content": "What is your favourite condiment?"},
	{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
	{"role": "user", "content": "Do you have mayonnaise recipes?"}
	]

	encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

	model_inputs = encodeds.to(device)
	model.to(device)

	generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
	decoded = tokenizer.batch_decode(generated_ids)
	print(decoded[0])
	```