Instructions to use johannhartmann/Obazda2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use johannhartmann/Obazda2 with Transformers:

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

pipe = pipeline("text-generation", model="johannhartmann/Obazda2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("johannhartmann/Obazda2")
model = AutoModelForCausalLM.from_pretrained("johannhartmann/Obazda2")
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 Settings

vLLM

How to use johannhartmann/Obazda2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "johannhartmann/Obazda2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "johannhartmann/Obazda2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/johannhartmann/Obazda2

SGLang

How to use johannhartmann/Obazda2 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 "johannhartmann/Obazda2" \
    --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": "johannhartmann/Obazda2",
		"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 "johannhartmann/Obazda2" \
        --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": "johannhartmann/Obazda2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use johannhartmann/Obazda2 with Docker Model Runner:
```
docker model run hf.co/johannhartmann/Obazda2
```

johannhartmann commited on Mar 4, 2024

Commit

cbb62f8

verified ·

1 Parent(s): 5cf53d2

Update README.md

Browse files

Files changed (1) hide show

README.md +20 -0

README.md CHANGED Viewed

@@ -14,6 +14,26 @@ tags:
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## Merge Details
 ### Merge Method

 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+# Benchmark
+This model has not been dpo-aligned yet.
+```json
+{
+    "first_turn": 6.79375,
+    "second_turn": 6.275,
+    "categories": {
+        "writing": 7.5,
+        "roleplay": 7.65,
+        "reasoning": 5.45,
+        "math": 2.55,
+        "coding": 4.8,
+        "extraction": 7.2,
+        "stem": 8.275,
+        "humanities": 8.85
+    },
+    "average": 6.534375000000001
+}
+```
 ## Merge Details
 ### Merge Method