Instructions to use TareksGraveyard/Wordsmith-V9-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksGraveyard/Wordsmith-V9-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="TareksGraveyard/Wordsmith-V9-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksGraveyard/Wordsmith-V9-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("TareksGraveyard/Wordsmith-V9-LLaMa-70B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TareksGraveyard/Wordsmith-V9-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TareksGraveyard/Wordsmith-V9-LLaMa-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TareksGraveyard/Wordsmith-V9-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksGraveyard/Wordsmith-V9-LLaMa-70B

SGLang

How to use TareksGraveyard/Wordsmith-V9-LLaMa-70B 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 "TareksGraveyard/Wordsmith-V9-LLaMa-70B" \
    --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": "TareksGraveyard/Wordsmith-V9-LLaMa-70B",
		"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 "TareksGraveyard/Wordsmith-V9-LLaMa-70B" \
        --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": "TareksGraveyard/Wordsmith-V9-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksGraveyard/Wordsmith-V9-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/TareksGraveyard/Wordsmith-V9-LLaMa-70B
```

Wordsmith-V9-LLaMa-70B / README.md

Tarek07

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	---
	base_model:
	- mlabonne/Hermes-3-Llama-3.1-70B-lorablated
	- Sao10K/L3.1-70B-Hanami-x1
	- nbeerbower/Llama3.1-Gutenberg-Doppel-70B
	- Sao10K/70B-L3.3-Cirrus-x1
	- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# MERGE2

	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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [mlabonne/Hermes-3-Llama-3.1-70B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-70B-lorablated) as a base.

	### Models Merged

	The following models were included in the merge:
	* [Sao10K/L3.1-70B-Hanami-x1](https://huggingface.co/Sao10K/L3.1-70B-Hanami-x1)
	* [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B)
	* [Sao10K/70B-L3.3-Cirrus-x1](https://huggingface.co/Sao10K/70B-L3.3-Cirrus-x1)
	* [EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1](https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: Sao10K/L3.1-70B-Hanami-x1
	parameters:
	weight: 0.25
	density: 0.7
	- model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
	parameters:
	weight: 0.25
	density: 0.7
	- model: Sao10K/70B-L3.3-Cirrus-x1
	parameters:
	weight: 0.20
	density: 0.7
	- model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
	parameters:
	weight: 0.20
	density: 0.7
	- model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated
	parameters:
	weight: 0.20
	density: 0.7
	merge_method: della_linear
	base_model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated
	parameters:
	epsilon: 0.2
	lambda: 1.1
	normalize: false
	dtype: bfloat16
	tokenizer:
	source: union
	```