Instructions to use jaigouk/go-bruins-ruby-bf16-function-calling-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaigouk/go-bruins-ruby-bf16-function-calling-v1 with Transformers:

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

pipe = pipeline("text-generation", model="jaigouk/go-bruins-ruby-bf16-function-calling-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaigouk/go-bruins-ruby-bf16-function-calling-v1")
model = AutoModelForCausalLM.from_pretrained("jaigouk/go-bruins-ruby-bf16-function-calling-v1")
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 jaigouk/go-bruins-ruby-bf16-function-calling-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaigouk/go-bruins-ruby-bf16-function-calling-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaigouk/go-bruins-ruby-bf16-function-calling-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaigouk/go-bruins-ruby-bf16-function-calling-v1

SGLang

How to use jaigouk/go-bruins-ruby-bf16-function-calling-v1 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 "jaigouk/go-bruins-ruby-bf16-function-calling-v1" \
    --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": "jaigouk/go-bruins-ruby-bf16-function-calling-v1",
		"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 "jaigouk/go-bruins-ruby-bf16-function-calling-v1" \
        --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": "jaigouk/go-bruins-ruby-bf16-function-calling-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaigouk/go-bruins-ruby-bf16-function-calling-v1 with Docker Model Runner:
```
docker model run hf.co/jaigouk/go-bruins-ruby-bf16-function-calling-v1
```

jaigouk commited on Dec 10, 2023

Commit

192b329

1 Parent(s): 03eb440

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+---
+license: apache-2.0
+base_model: jaigouk/go-bruins-bf16-ruby
+tags:
+- generated_from_trainer
+model-index:
+- name: go-bruins-ruby-bf16
+  results: []
+language:
+- en
+---
+# go-bruins-ruby-bf16
+This model is a fine-tuned version of [rwitz/go-bruins](https://huggingface.co/rwitz/go-bruins) on an unknown dataset.
+## Model description
+- intention is to use this with system message for generating ruby codes
+## Intended uses & limitations
+- writing rspec / ruby codes
+## Training and evaluation data
+- https://huggingface.co/datasets/jaigouk/coding-dataset
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_steps: 1
+- num_epochs: 1
+### Training results
+### Framework versions
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1