Instructions to use Jolly-Q/70B_unstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jolly-Q/70B_unstruct with Transformers:

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

pipe = pipeline("text-generation", model="Jolly-Q/70B_unstruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jolly-Q/70B_unstruct")
model = AutoModelForCausalLM.from_pretrained("Jolly-Q/70B_unstruct")
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

vLLM

How to use Jolly-Q/70B_unstruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Jolly-Q/70B_unstruct

SGLang

How to use Jolly-Q/70B_unstruct 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 "Jolly-Q/70B_unstruct" \
    --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": "Jolly-Q/70B_unstruct",
		"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 "Jolly-Q/70B_unstruct" \
        --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": "Jolly-Q/70B_unstruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jolly-Q/70B_unstruct with Docker Model Runner:
```
docker model run hf.co/Jolly-Q/70B_unstruct
```

schonsense commited on Jun 21, 2025

Commit

07dece6

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@@ -16,8 +16,10 @@ This is an attempt to take llama 3.3 instruct and peel back some of it's instruc
 In theory this creates a ~75% refusal abliterated model with ~70% of it's instruct following capabilities intact, healed some in addition to having its instruct overtuning rolled back ~50%.
 Key points:
-Δw task vector for the abiliteration model is exclusively the opposed refusal vectors.
-Δw task vector for 3.1 base is the reversal of all of the instruct and instruct related tuning to move from 3.1 to 3.3 inst.

 In theory this creates a ~75% refusal abliterated model with ~70% of it's instruct following capabilities intact, healed some in addition to having its instruct overtuning rolled back ~50%.
 Key points:
+# Δw task vector for the abiliteration model is exclusively the opposed refusal vectors.
+# Δw task vector for 3.1 base is the reversal of all of the instruct and instruct related tuning to move from 3.1 to 3.3 inst.