Instructions to use anna-tch/unsloth-mistral-wine-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anna-tch/unsloth-mistral-wine-sft with Transformers:

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

pipe = pipeline("text-generation", model="anna-tch/unsloth-mistral-wine-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("anna-tch/unsloth-mistral-wine-sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anna-tch/unsloth-mistral-wine-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/anna-tch/unsloth-mistral-wine-sft

SGLang

How to use anna-tch/unsloth-mistral-wine-sft 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 "anna-tch/unsloth-mistral-wine-sft" \
    --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": "anna-tch/unsloth-mistral-wine-sft",
		"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 "anna-tch/unsloth-mistral-wine-sft" \
        --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": "anna-tch/unsloth-mistral-wine-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use anna-tch/unsloth-mistral-wine-sft with Docker Model Runner:
```
docker model run hf.co/anna-tch/unsloth-mistral-wine-sft
```

anna-tch/unsloth-mistral-wine-sft

Wine tasting-note language model (SFT) fine-tuned with Unsloth.


Trained at	`2026-08-26T09:32:31Z`
Phase	`sft`
Base model	`unsloth/Mistral-Small-24B-Instruct-2501-bnb-4bit`
Dataset	`anna-tch/tastee-notes-sft-dataset`

Repo name is stable across runs; train sizes and hyperparams for this revision are listed below (also in the Hub commit message).

Training config (this revision)

trained_at=2026-08-26T09:32:31Z
phase=sft
base_model=unsloth/Mistral-Small-24B-Instruct-2501-bnb-4bit
dataset=anna-tch/tastee-notes-sft-dataset
chat_template=mistral
max_seq_length=4096
load_in_4bit=True
save_method=merged_16bit
lora_r=128
lora_alpha=32
lora_dropout=0.1
use_rslora=True
target_modules=q_proj,k_proj,v_proj,o_proj
max_steps=400
batch_size=8
grad_accum=2
effective_batch=16
learning_rate=5e-05
embedding_lr=1e-05
warmup_steps=10
weight_decay=0.01
optim=adamw_8bit
lr_scheduler_type=linear
seed=3407
completion_only=True
eval_steps=20
early_stopping_patience=5
best_checkpoint=outputs/2026-08-26/Mistral-Small-24B-Instruct-2501-bnb-4bit/wine-inst/checkpoint-160
train_rows=1438
val_rows=360
train_loss=0.3091
eval_loss=0.6007651686668396
output_dir=outputs/2026-08-26/Mistral-Small-24B-Instruct-2501-bnb-4bit/wine-inst

Usage

from unsloth import FastLanguageModel

model_id = "anna-tch/unsloth-mistral-wine-sft"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_id,
    max_seq_length=4096,
    load_in_4bit=True,
)

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for anna-tch/unsloth-mistral-wine-sft

Base model

mistralai/Mistral-Small-24B-Base-2501

Finetuned

mistralai/Mistral-Small-24B-Instruct-2501

Quantized

unsloth/Mistral-Small-24B-Instruct-2501-bnb-4bit

Finetuned

(1)

this model