Instructions to use ehristoforu/Gixtral-100B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ehristoforu/Gixtral-100B with Transformers:

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

pipe = pipeline("text-generation", model="ehristoforu/Gixtral-100B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ehristoforu/Gixtral-100B")
model = AutoModelForCausalLM.from_pretrained("ehristoforu/Gixtral-100B")
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 ehristoforu/Gixtral-100B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ehristoforu/Gixtral-100B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ehristoforu/Gixtral-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ehristoforu/Gixtral-100B

SGLang

How to use ehristoforu/Gixtral-100B 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 "ehristoforu/Gixtral-100B" \
    --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": "ehristoforu/Gixtral-100B",
		"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 "ehristoforu/Gixtral-100B" \
        --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": "ehristoforu/Gixtral-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ehristoforu/Gixtral-100B with Docker Model Runner:
```
docker model run hf.co/ehristoforu/Gixtral-100B
```

Gixtral 100B (Mixtral from 8x22B & 8x7B to 100B)

We created a model from other cool models to combine everything into one cool model.

Model Details

Model Description

Developed by: @ehristoforu
Model type: Text Generation (conversational)
Language(s) (NLP): English, French, German, Spanish, Italian
Finetuned from model: mistralai/Mixtral-8x22B-Instruct-v0.1 & mistralai/Mixtral-8x7B-Instruct-v0.1

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ehristoforu/Gixtral-100B"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

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?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")

outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

About merge

Base model: mistralai/Mixtral-8x22B-Instruct-v0.1 & mistralai/Mixtral-8x7B-Instruct-v0.1

Merge models:

mistralai/Mixtral-8x22B-Instruct-v0.1
mistralai/Mixtral-8x7B-Instruct-v0.1
cognitivecomputations/dolphin-2.7-mixtral-8x7b
alpindale/WizardLM-2-8x22B

Merge datasets:

ehartford/dolphin
jondurbin/airoboros-2.2.1
ehartford/dolphin-coder
migtissera/Synthia-v1.3
teknium/openhermes
ise-uiuc/Magicoder-OSS-Instruct-75K
ise-uiuc/Magicoder-Evol-Instruct-110K
LDJnr/Pure-Dove

Downloads last month: 9

Safetensors

Model size

98B params

Tensor type

BF16

Model tree for ehristoforu/Gixtral-100B

alpindale/WizardLM-2-8x22B

dphn/dolphin-2.7-mixtral-8x7b

mistralai/Mixtral-8x22B-Instruct-v0.1

mistralai/Mixtral-8x7B-Instruct-v0.1

Merge model

this model

ehristoforu
/

Gixtral-100B