Instructions to use athirdpath/BigMistral-11b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athirdpath/BigMistral-11b with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/BigMistral-11b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/BigMistral-11b")
model = AutoModelForCausalLM.from_pretrained("athirdpath/BigMistral-11b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use athirdpath/BigMistral-11b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athirdpath/BigMistral-11b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/BigMistral-11b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/athirdpath/BigMistral-11b

SGLang

How to use athirdpath/BigMistral-11b 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 "athirdpath/BigMistral-11b" \
    --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": "athirdpath/BigMistral-11b",
		"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 "athirdpath/BigMistral-11b" \
        --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": "athirdpath/BigMistral-11b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use athirdpath/BigMistral-11b with Docker Model Runner:
```
docker model run hf.co/athirdpath/BigMistral-11b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EDIT: Base Mistral, but with some minor head trauma. Promising in theory, needs finetuning, but only really outperforms the 14b in size.

A 11b Mistral base model, based on the NeverSleep recipe.

Recipe

slices

sources:
- model: mistralai/Mistral-7B-v0.1
- layer_range: [0, 24]
sources:
- model: mistralai/Mistral-7B-v0.1
- layer_range: [8, 32]

merge_method: passthrough

dtype: bfloat16

Downloads last month: 8

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for athirdpath/BigMistral-11b

Quantizations

1 model