Instructions to use TareksGraveyard/Progenitor-V1.3-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksGraveyard/Progenitor-V1.3-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="TareksGraveyard/Progenitor-V1.3-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksGraveyard/Progenitor-V1.3-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("TareksGraveyard/Progenitor-V1.3-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/Progenitor-V1.3-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TareksGraveyard/Progenitor-V1.3-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/Progenitor-V1.3-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksGraveyard/Progenitor-V1.3-LLaMa-70B

SGLang

How to use TareksGraveyard/Progenitor-V1.3-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/Progenitor-V1.3-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/Progenitor-V1.3-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/Progenitor-V1.3-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/Progenitor-V1.3-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksGraveyard/Progenitor-V1.3-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/TareksGraveyard/Progenitor-V1.3-LLaMa-70B
```

Tarek07 commited on Feb 2, 2025

Commit

83d4a41

verified ·

1 Parent(s): f22ea1c

Update README.md

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@@ -12,7 +12,7 @@ tags:
 - merge
 license: llama3.3
 ---
-After a lot of testing by myself and others the general consensus was that Progenitor 1.1 (della_linear) was better than 1.2 (SCE). I actually got some advice from Steelskull about how I had my top k parameter way too high. So I decided to lower it for this merge. Now because I feel 1.2 was still a decent model with the top k on 1, I halved the top k parameter to 0.50 which is basically the max recommended setting.
 # merge
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

 - merge
 license: llama3.3
 ---
+After a lot of testing by myself and others the general consensus was that Progenitor 1.1 (della_linear) was better than 1.2 (SCE). I actually got some advice from Steelskull about how I had my top k parameter way too high. So I decided to lower it for this merge. Now because I feel 1.2 was still a decent model with the top k on 1, I halved the top k parameter to 0.50 which is basically the max recommended setting. From early testing, I am not sure...it's not blowing me away.
 # merge
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).