Instructions to use tjcrims0n/ms7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tjcrims0n/ms7 with Transformers:

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

pipe = pipeline("text-generation", model="tjcrims0n/ms7")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tjcrims0n/ms7")
model = AutoModelForCausalLM.from_pretrained("tjcrims0n/ms7")
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 tjcrims0n/ms7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tjcrims0n/ms7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tjcrims0n/ms7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tjcrims0n/ms7

SGLang

How to use tjcrims0n/ms7 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 "tjcrims0n/ms7" \
    --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": "tjcrims0n/ms7",
		"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 "tjcrims0n/ms7" \
        --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": "tjcrims0n/ms7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tjcrims0n/ms7 with Docker Model Runner:
```
docker model run hf.co/tjcrims0n/ms7
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ms7

Full Transformers safetensors model based on mistralai/Mistral-7B-Instruct-v0.3.

Notes

This is a Mistral-family model, not a Cerberus-uncensored category label.
annihilate_merge_metadata.json records the local annihilation/export path.
This repo is a full model export, not a PEFT/LoRA adapter.
The model uses the standard Mistral chat template and built-in Transformers MistralForCausalLM loader.

Quick Load

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "tjcrims0n/ms7"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

messages = [{"role": "user", "content": "Hello."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=80, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Smoke Test Notes

A local smoke test confirmed the repo loads with the built-in Mistral loader and produces coherent text. The arithmetic quirk 17 * 23 -> 381 also reproduces on the untouched cached base model, so it is not evidence of a broken annihilation export.

Downloads last month: 14

Safetensors

Model size

7B params

Tensor type

F16

Model tree for tjcrims0n/ms7

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

(508)

this model

Quantizations

1 model