Instructions to use ishiki-labs/mistral-7b-ami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ishiki-labs/mistral-7b-ami with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base_model, "ishiki-labs/mistral-7b-ami")

Transformers

How to use ishiki-labs/mistral-7b-ami with Transformers:

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

pipe = pipeline("text-generation", model="ishiki-labs/mistral-7b-ami")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ishiki-labs/mistral-7b-ami", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ishiki-labs/mistral-7b-ami with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ishiki-labs/mistral-7b-ami

SGLang

How to use ishiki-labs/mistral-7b-ami 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 "ishiki-labs/mistral-7b-ami" \
    --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": "ishiki-labs/mistral-7b-ami",
		"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 "ishiki-labs/mistral-7b-ami" \
        --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": "ishiki-labs/mistral-7b-ami",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ishiki-labs/mistral-7b-ami with Docker Model Runner:
```
docker model run hf.co/ishiki-labs/mistral-7b-ami
```

kraken07 commited on Mar 13

Commit

1bf37e6

verified ·

1 Parent(s): 6dd146c

Update README.md

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@@ -39,4 +39,19 @@ model = PeftModel.from_pretrained(base_model, "kraken07/mistral-7b-ami")
 tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
 # Your input format should match training: context turns + current turn
-# Output: SPEAK or SILENT prediction for the target speaker

 tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
 # Your input format should match training: context turns + current turn
+# Output: SPEAK or SILENT prediction for the target speaker
+```
+## Citation
+If you use this model, please cite our work:
+```bibtex
+@misc{bhagtani2026speakstaysilentcontextaware,
+  title={Speak or Stay Silent: Context-Aware Turn-Taking in Multi-Party Dialogue},
+  author={Bhagtani, Kratika and Anand, Mrinal and Xu, Yu Chen and Yadav, Amit Kumar Singh},
+  year={2026},
+  archivePrefix={arXiv},
+  url={https://arxiv.org/abs/2603.11409}
+}
+```