Instructions to use kittn/mistral-7B-v0.1-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kittn/mistral-7B-v0.1-hf with Transformers:

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

pipe = pipeline("text-generation", model="kittn/mistral-7B-v0.1-hf")

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

tokenizer = AutoTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = AutoModelForCausalLM.from_pretrained("kittn/mistral-7B-v0.1-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kittn/mistral-7B-v0.1-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kittn/mistral-7B-v0.1-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kittn/mistral-7B-v0.1-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kittn/mistral-7B-v0.1-hf

SGLang

How to use kittn/mistral-7B-v0.1-hf 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 "kittn/mistral-7B-v0.1-hf" \
    --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": "kittn/mistral-7B-v0.1-hf",
		"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 "kittn/mistral-7B-v0.1-hf" \
        --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": "kittn/mistral-7B-v0.1-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kittn/mistral-7B-v0.1-hf with Docker Model Runner:
```
docker model run hf.co/kittn/mistral-7B-v0.1-hf
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

UPDATE: Official version is out, use it instead: https://huggingface.co/mistralai/Mistral-7B-v0.1

mistral-7B-v0.1-hf

Huggingface compatible version of Mistral's 7B model: https://twitter.com/MistralAI/status/1706877320844509405

Usage

Load in bfloat16 (16GB VRAM or higher)

import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)

Load in bitsandbytes nf4 (6GB VRAM or higher, maybe less with double_quant)

import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    device_map={"": 0},
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=False, # set to True to save more VRAM at the cost of some speed/accuracy
    ),
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)

Load in bitsandbytes int8 (8GB VRAM or higher). Quite slow; not recommended.

import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    device_map={"": 0},
    quantization_config=BitsAndBytesConfig(
        load_in_8bit=True,
    ),
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)

Notes

The original huggingface conversion script converts the model from bf16 to fp16 before saving it. This script doesn't
The tokenizer is created with legacy=False, more about this here
Saved in safetensors format

Conversion script [link]

Unlike meta-llama/Llama-2-7b, this model uses GQA. This breaks some assumptions in the original conversion script, requiring a few changes.

Conversion script: link

Original conversion script: link

Downloads last month: 72

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for kittn/mistral-7B-v0.1-hf

Quantizations

1 model