Instructions to use augtoma/qCammel-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use augtoma/qCammel-13 with Transformers:

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

pipe = pipeline("text-generation", model="augtoma/qCammel-13")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("augtoma/qCammel-13")
model = AutoModelForCausalLM.from_pretrained("augtoma/qCammel-13")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use augtoma/qCammel-13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "augtoma/qCammel-13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "augtoma/qCammel-13",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/augtoma/qCammel-13

SGLang

How to use augtoma/qCammel-13 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 "augtoma/qCammel-13" \
    --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": "augtoma/qCammel-13",
		"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 "augtoma/qCammel-13" \
        --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": "augtoma/qCammel-13",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use augtoma/qCammel-13 with Docker Model Runner:
```
docker model run hf.co/augtoma/qCammel-13
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

qCammel-13

qCammel-13 is a fine-tuned version of Llama-2 13B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities.

Model Details

Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the website and accept their License before downloading this model .

The fine-tuning process applied to qCammel-13 involves a distilled dataset of 15,000 instructions and is trained with QLoRA,

Variations The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 13B model.

Input Models input text only.

Output Models generate text only.

Model Architecture qCammel-13 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture.

License A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/ Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved

Research Papers

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Papers for augtoma/qCammel-13

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61

Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

Paper • 2305.12031 • Published May 19, 2023 • 5

LLaMA: Open and Efficient Foundation Language Models

Paper • 2302.13971 • Published Feb 27, 2023 • 23