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

Libraries

How to use QuixiAI/samantha-mistral-7b with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/samantha-mistral-7b")

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

tokenizer = AutoTokenizer.from_pretrained("QuixiAI/samantha-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("QuixiAI/samantha-mistral-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuixiAI/samantha-mistral-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuixiAI/samantha-mistral-7b

SGLang

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

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

cocoloco31 commited on Oct 2, 2025

Commit

a630074

verified ·

1 Parent(s): fac7704

Update read.me

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@@ -1,5 +1,14 @@
 ---
 license: apache-2.0
 ---
 Trained on [mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base model, this Samantha was trained in 2 hours on 4x A100 80gb with 20 epochs of the Samantha-1.1 dataset.
@@ -62,4 +71,4 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 | TruthfulQA (0-shot)   | 46.08   |
 | Winogrande (5-shot)   | 76.8   |
 | GSM8K (5-shot)        | 16.0        |
-| DROP (3-shot)         | 11.22         |

 ---
 license: apache-2.0
+language:
+- en
+- zh
+- es
+base_model:
+- Qwen/Qwen3-Next-80B-A3B-Instruct
+library_name: adapter-transformers
+tags:
+- agent
 ---
 Trained on [mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base model, this Samantha was trained in 2 hours on 4x A100 80gb with 20 epochs of the Samantha-1.1 dataset.
 | TruthfulQA (0-shot)   | 46.08   |
 | Winogrande (5-shot)   | 76.8   |
 | GSM8K (5-shot)        | 16.0        |
+| DROP (3-shot)         | 11.22         |