Instructions to use Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B with Transformers:

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

pipe = pipeline("text-generation", model="Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B")
model = AutoModelForCausalLM.from_pretrained("Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B", device_map="auto")
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 Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B

SGLang

How to use Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-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 "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B" \
    --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": "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B",
		"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 "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B" \
        --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": "Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B with Docker Model Runner:
```
docker model run hf.co/Severian/Mistral-v0.2-Nexus-Internal-Knowledge-Map-7B
```

Further Fine-Tune Joseph717171/Mistral-10.7B-v0.2 on IKM and ANIMA-Phi-Neptune

by Joseph717171 - opened Mar 30, 2024

Discussion

Joseph717171

Mar 30, 2024

•

edited Mar 30, 2024

Severian, I made a Depth Up-Scaled version of Mistral-7B-v0.2 Base model (Joseph717171/Mistral-10.7B-v0.2). It just needs to be further fine-tuned - perhaps on your IKM dataset for Nexus-IKM and ANIMA-Phi-Neptune? I hope that this helps you with your goals and experimentations. Cheers!
🎉🚀

Severian

Owner Mar 31, 2024

Thank you so much! Currently working on these new trainings, hopefully will have some good working version (at least GGUFs) up soon. Balancing quite a few different projects at the moment : )

Joseph717171

Mar 31, 2024

You're welcome! Don't work yourself too hard! Burn out is disastrous. We need you happy and healthy. 😁

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