Instructions to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF", device_map="auto")

llama-cpp-python

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF",
	filename="Mistral-7B-Instruct-v0.1-IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF 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 "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF" \
    --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": "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF",
		"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 "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF" \
        --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": "duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.1-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Quantizations of https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1

From original readme

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"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!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

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])

Troubleshooting

If you see the following error:

Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'

Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers

This should not be required after transformers-v4.33.4.

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

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