Instructions to use jeiku/Orthocopter_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/Orthocopter_8B with Transformers:

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

pipe = pipeline("text-generation", model="jeiku/Orthocopter_8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeiku/Orthocopter_8B")
model = AutoModelForCausalLM.from_pretrained("jeiku/Orthocopter_8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jeiku/Orthocopter_8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeiku/Orthocopter_8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeiku/Orthocopter_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeiku/Orthocopter_8B

SGLang

How to use jeiku/Orthocopter_8B 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 "jeiku/Orthocopter_8B" \
    --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": "jeiku/Orthocopter_8B",
		"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 "jeiku/Orthocopter_8B" \
        --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": "jeiku/Orthocopter_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeiku/Orthocopter_8B with Docker Model Runner:
```
docker model run hf.co/jeiku/Orthocopter_8B
```

jeiku commited on May 10, 2024

Commit

19df985

verified ·

1 Parent(s): 4fb7199

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -39

README.md CHANGED Viewed

@@ -1,46 +1,15 @@
 ---
-tags:
-- merge
-- mergekit
-- lazymergekit
 ---
-# Orthocopter_8B
-Orthocopter_8B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-## 🧩 Configuration
-```yaml
-models:
-  - model: Edgerunners/meta-llama-3-8b-instruct-hf-ortho-baukit-10fail-1000total+ResplendentAI/Aura_Llama3
-  - model: Edgerunners/meta-llama-3-8b-instruct-hf-ortho-baukit-10fail-1000total+ResplendentAI/Luna_Llama3
-merge_method: model_stock
-base_model: Edgerunners/meta-llama-3-8b-instruct-hf-ortho-baukit-10fail-1000total
-dtype: float16
-```
-## 💻 Usage
-```python
-!pip install -qU transformers accelerate
-from transformers import AutoTokenizer
-import transformers
-import torch
-model = "jeiku/Orthocopter_8B"
-messages = [{"role": "user", "content": "What is a large language model?"}]
-tokenizer = AutoTokenizer.from_pretrained(model)
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
-```

 ---
+license: apache-2.0
+language:
+- en
 ---
+# Orthocopter
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/B1V2QhXSqtBRHT6MW6cCD.png)
+This model is thanks to the hard work of lucyknada with the Edgerunners. Her work produced the following model, which I used as the base:
+https://huggingface.co/Edgerunners/meta-llama-3-8b-instruct-hf-ortho-baukit-10fail-1000total
+I then applied two handwritten datasets over top of this and the results are pretty nice, with no refusals and plenty of personality.