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

Libraries

How to use ArHFcloud/mistral-7b-detell with Transformers:

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

pipe = pipeline("text-generation", model="ArHFcloud/mistral-7b-detell")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArHFcloud/mistral-7b-detell")
model = AutoModelForCausalLM.from_pretrained("ArHFcloud/mistral-7b-detell")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArHFcloud/mistral-7b-detell with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ArHFcloud/mistral-7b-detell

SGLang

How to use ArHFcloud/mistral-7b-detell 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 "ArHFcloud/mistral-7b-detell" \
    --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": "ArHFcloud/mistral-7b-detell",
		"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 "ArHFcloud/mistral-7b-detell" \
        --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": "ArHFcloud/mistral-7b-detell",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

mistral-7b-detell

MCQuilibrium de-tell model. A QLoRA fine-tune of Mistral-7B-Instruct-v0.2 that rewrites the answer choices of a multiple-choice question to remove tells, the surface artifacts that leak the correct answer. The LoRA adapter has been merged into the base weights, so this repo loads directly.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("ArHFcloud/mistral-7b-detell")
model = AutoModelForCausalLM.from_pretrained("ArHFcloud/mistral-7b-detell", device_map="auto")

messages = [{"role": "user", "content": "...question and choices..."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Downloads last month: 149

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ArHFcloud/mistral-7b-detell

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1095)

this model