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

Libraries

How to use NextGLab/ORANSight_Mistral_Nemo_Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NextGLab/ORANSight_Mistral_Nemo_Instruct")
model = AutoModelForCausalLM.from_pretrained("NextGLab/ORANSight_Mistral_Nemo_Instruct")
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

vLLM

How to use NextGLab/ORANSight_Mistral_Nemo_Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NextGLab/ORANSight_Mistral_Nemo_Instruct

SGLang

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

Docker Model Runner
How to use NextGLab/ORANSight_Mistral_Nemo_Instruct with Docker Model Runner:
```
docker model run hf.co/NextGLab/ORANSight_Mistral_Nemo_Instruct
```

prnshv commited on Jan 12, 2025

Commit

d534226

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1 Parent(s): 74b554a

Update README.md

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@@ -1,23 +1,50 @@
 ---
-base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
 tags:
 - text-generation-inference
 - transformers
-- unsloth
 - mistral
-- trl
-- sft
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** prnshv
 - **License:** apache-2.0
-- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
-This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
 tags:
 - text-generation-inference
 - transformers
 - mistral
 license: apache-2.0
 language:
 - en
 ---
+# Model Card for ORANSight Mistral-12B (Nemo)
+This model belongs to the first release of the ORANSight family of models.
+- **Developed by:** NextG lab@ NC State
 - **License:** apache-2.0
+- **Context Window:** 128K
+- **Fine Tuning Framework:** Unsloth
+### Generate with Transformers
+Below is a quick example of how to use the model with Hugging Face Transformers:
+```python
+from transformers import pipeline
+# Example query
+messages = [
+    {"role": "system", "content": "You are an O-RAN expert assistant."},
+    {"role": "user", "content": "Explain the E2 interface."},
+]
+# Load the model
+chatbot = pipeline("text-generation", model="prnshv/ORANSight_Mistral_Nemo_Instruct")
+result = chatbot(messages)
+print(result)
+```
+### Coming Soon
+A detailed paper documenting the experiments and results achieved with this model will be available soon. Meanwhile, if you try this model, please cite the below mentioned paper to acknowledge the foundational work that enabled this fine-tuning.
+```bibtex
+@article{gajjar2024oran,
+  title={Oran-bench-13k: An open source benchmark for assessing llms in open radio access networks},
+  author={Gajjar, Pranshav and Shah, Vijay K},
+  journal={arXiv preprint arXiv:2407.06245},
+  year={2024}
+}
+```
+---