Instructions to use tannedbum/Ellaria-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tannedbum/Ellaria-9B with Transformers:

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

pipe = pipeline("text-generation", model="tannedbum/Ellaria-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tannedbum/Ellaria-9B")
model = AutoModelForCausalLM.from_pretrained("tannedbum/Ellaria-9B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tannedbum/Ellaria-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tannedbum/Ellaria-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tannedbum/Ellaria-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tannedbum/Ellaria-9B

SGLang

How to use tannedbum/Ellaria-9B 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 "tannedbum/Ellaria-9B" \
    --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": "tannedbum/Ellaria-9B",
		"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 "tannedbum/Ellaria-9B" \
        --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": "tannedbum/Ellaria-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tannedbum/Ellaria-9B with Docker Model Runner:
```
docker model run hf.co/tannedbum/Ellaria-9B
```

tannedbum commited on Aug 4, 2024

Commit

070bbbc

verified ·

1 Parent(s): 3015886

Update README.md

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README.md +76 -52

README.md CHANGED Viewed

@@ -1,52 +1,76 @@
----
-base_model:
-- princeton-nlp/gemma-2-9b-it-SimPO
-- TheDrummer/Gemmasutra-9B-v1
-library_name: transformers
-tags:
-- mergekit
-- merge
----
-# Ellaria-9B
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the SLERP merge method.
-### Models Merged
-The following models were included in the merge:
-* [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)
-* [TheDrummer/Gemmasutra-9B-v1](https://huggingface.co/TheDrummer/Gemmasutra-9B-v1)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-slices:
-  - sources:
-      - model: TheDrummer/Gemmasutra-9B-v1
-        layer_range: [0, 42]
-      - model: princeton-nlp/gemma-2-9b-it-SimPO
-        layer_range: [0, 42]
-merge_method: slerp
-base_model: TheDrummer/Gemmasutra-9B-v1
-parameters:
-  t:
-    - filter: self_attn
-      value: [0.2, 0.4, 0.6, 0.2, 0.4]
-    - filter: mlp
-      value: [0.8, 0.6, 0.4, 0.8, 0.6]
-    - value: 0.4
-dtype: bfloat16
-```

+---
+base_model:
+- princeton-nlp/gemma-2-9b-it-SimPO
+- TheDrummer/Gemmasutra-9B-v1
+library_name: transformers
+tags:
+- mergekit
+- merge
+- roleplay
+- sillytavern
+- gemma2
+- not-for-all-audiences
+license: cc-by-nc-4.0
+language:
+- en
+---
+## SillyTavern
+## Text Completion presets
+```
+temp 0.9
+top_k 30
+top_p 0.75
+min_p 0.2
+rep_pen 1.1
+smooth_factor 0.25
+smooth_curve 1
+```
+## Advanced Formatting
+[Instruct preset by Virt-io](https://huggingface.co/Virt-io/SillyTavern-Presets/tree/main/Prompts/LLAMA-3/v1.9)
+Target length (tokens): 192
+Instruct Mode: Enabled
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the SLERP merge method.
+### Models Merged
+The following models were included in the merge:
+* [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)
+* [TheDrummer/Gemmasutra-9B-v1](https://huggingface.co/TheDrummer/Gemmasutra-9B-v1)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+slices:
+  - sources:
+      - model: TheDrummer/Gemmasutra-9B-v1
+        layer_range: [0, 42]
+      - model: princeton-nlp/gemma-2-9b-it-SimPO
+        layer_range: [0, 42]
+merge_method: slerp
+base_model: TheDrummer/Gemmasutra-9B-v1
+parameters:
+  t:
+    - filter: self_attn
+      value: [0.2, 0.4, 0.6, 0.2, 0.4]
+    - filter: mlp
+      value: [0.8, 0.6, 0.4, 0.8, 0.6]
+    - value: 0.4
+dtype: bfloat16
+```