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

Libraries

How to use KasparZ/Mistral-7B-v0.1-hitl with Transformers:

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

pipe = pipeline("text-generation", model="KasparZ/Mistral-7B-v0.1-hitl")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KasparZ/Mistral-7B-v0.1-hitl")
model = AutoModelForCausalLM.from_pretrained("KasparZ/Mistral-7B-v0.1-hitl", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KasparZ/Mistral-7B-v0.1-hitl 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 KasparZ/Mistral-7B-v0.1-hitl
# Run inference directly in the terminal:
llama cli -hf KasparZ/Mistral-7B-v0.1-hitl

Install from WinGet (Windows)

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

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 KasparZ/Mistral-7B-v0.1-hitl
# Run inference directly in the terminal:
./llama-cli -hf KasparZ/Mistral-7B-v0.1-hitl

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 KasparZ/Mistral-7B-v0.1-hitl
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KasparZ/Mistral-7B-v0.1-hitl

Use Docker

docker model run hf.co/KasparZ/Mistral-7B-v0.1-hitl

LM Studio
Jan

vLLM

How to use KasparZ/Mistral-7B-v0.1-hitl with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KasparZ/Mistral-7B-v0.1-hitl

SGLang

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

Ollama
How to use KasparZ/Mistral-7B-v0.1-hitl with Ollama:
```
ollama run hf.co/KasparZ/Mistral-7B-v0.1-hitl
```

Unsloth Studio

How to use KasparZ/Mistral-7B-v0.1-hitl 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 KasparZ/Mistral-7B-v0.1-hitl 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 KasparZ/Mistral-7B-v0.1-hitl to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use KasparZ/Mistral-7B-v0.1-hitl with Docker Model Runner:
```
docker model run hf.co/KasparZ/Mistral-7B-v0.1-hitl
```

Lemonade

How to use KasparZ/Mistral-7B-v0.1-hitl with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KasparZ/Mistral-7B-v0.1-hitl

Run and chat with the model

lemonade run user.Mistral-7B-v0.1-hitl-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

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

Check out the documentation for more information.

base_model = "mistralai/Mistral-7B-v0.1" dataset_name = "KasparZ/HITL-2"

load_in_4bit= True, bnb_4bit_quant_type= "nf4", bnb_4bit_compute_dtype= torch.bfloat16, bnb_4bit_use_double_quant= False,

device_map="auto", trust_remote_code=True,

lora_alpha=16, lora_dropout=0.1, r=64, bias="none", task_type="CAUSAL_LM", target_modules=["q_proj", "k_proj", "v_proj", "o_proj","gate_proj"]

model.gradient_checkpointing_enable()

tokenizer.padding_side = 'right' tokenizer.add_eos_token = True

num_train_epochs=4,
per_device_train_batch_size=4,
gradient_accumulation_steps=1,
optim="paged_adamw_32bit",
save_steps=50,
logging_steps=1,
learning_rate=2e-4,
weight_decay=0.001,
fp16=False,
bf16=False,
max_grad_norm=0.3,
max_steps=-1,
warmup_ratio=0.03,
group_by_length=True,
lr_scheduler_type="constant",

Downloads last month: 14

Safetensors

Model size

7B params

Tensor type

F16