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

Libraries

How to use theophilusowiti/Caracal_instruct with Transformers:

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

pipe = pipeline("text-generation", model="theophilusowiti/Caracal_instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theophilusowiti/Caracal_instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("theophilusowiti/Caracal_instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theophilusowiti/Caracal_instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/theophilusowiti/Caracal_instruct

SGLang

How to use theophilusowiti/Caracal_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 "theophilusowiti/Caracal_instruct" \
    --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": "theophilusowiti/Caracal_instruct",
		"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 "theophilusowiti/Caracal_instruct" \
        --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": "theophilusowiti/Caracal_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

theophilusowiti commited on May 31

Commit

6604b95

verified ·

1 Parent(s): aa82511

End of training

Browse files

Files changed (3) hide show

README.md +63 -0
generation_config.json +6 -0
pytorch_model.bin +1 -1

README.md ADDED Viewed

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+---
+library_name: transformers
+license: cc-by-nc-4.0
+base_model: theophilusowiti/Caracal_GPT
+tags:
+- generated_from_trainer
+model-index:
+- name: Caracal_instruct
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# Caracal_instruct
+This model is a fine-tuned version of [theophilusowiti/Caracal_GPT](https://huggingface.co/theophilusowiti/Caracal_GPT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.6072
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 3
+### Training results
+| Training Loss | Epoch  | Step  | Validation Loss |
+|:-------------:|:------:|:-----:|:---------------:|
+| 3.1297        | 0.9999 | 6036  | 2.6244          |
+| 2.7599        | 2.0    | 12073 | 2.6072          |
+| 2.7805        | 2.9998 | 18108 | 2.6143          |
+### Framework versions
+- Transformers 4.45.0
+- Pytorch 2.12.0+cu126
+- Datasets 4.8.5
+- Tokenizers 0.20.3

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.0"
+}

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