Instructions to use CognitiveLab/Fireship-clone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CognitiveLab/Fireship-clone with Transformers:

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

pipe = pipeline("text-generation", model="CognitiveLab/Fireship-clone")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CognitiveLab/Fireship-clone")
model = AutoModelForCausalLM.from_pretrained("CognitiveLab/Fireship-clone")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CognitiveLab/Fireship-clone with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CognitiveLab/Fireship-clone

SGLang

How to use CognitiveLab/Fireship-clone 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 "CognitiveLab/Fireship-clone" \
    --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": "CognitiveLab/Fireship-clone",
		"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 "CognitiveLab/Fireship-clone" \
        --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": "CognitiveLab/Fireship-clone",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CognitiveLab/Fireship-clone with Docker Model Runner:
```
docker model run hf.co/CognitiveLab/Fireship-clone
```

AdithyaSK HF Staff commited on Feb 5, 2024

Commit

1cd827e

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1 Parent(s): 38f7d12

Update README.md

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@@ -7,13 +7,6 @@ model-index:
   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. -->
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-<details><summary>See axolotl config</summary>
-axolotl version: `0.3.0`
 ```yaml
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM

   results: []
 ---
 ```yaml
 base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM