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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jolly-Q/70B_SOG_unstructed")
model = AutoModelForCausalLM.from_pretrained("Jolly-Q/70B_SOG_unstructed")
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_SOG_unstructed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jolly-Q/70B_SOG_unstructed"
# 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_SOG_unstructed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

schonsense commited on Jul 9, 2025

Commit

a838d27

verified ·

1 Parent(s): 5dc295f

Update README.md

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README.md +69 -67

README.md CHANGED Viewed

@@ -1,67 +1,69 @@
----
-base_model:
-- schonsense/SOG_10k_70B
-- meta-llama/Llama-3.1-70B
-- meta-llama/Llama-3.3-70B-Instruct
-- schonsense/70B_unstructWR
-library_name: transformers
-tags:
-- mergekit
-- merge
----
-# 70B_SOG_unstructed
-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 [DELLA](https://arxiv.org/abs/2406.11617) merge method using [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as a base.
-### Models Merged
-The following models were included in the merge:
-* [schonsense/SOG_10k_70B](https://huggingface.co/schonsense/SOG_10k_70B)
-* [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B)
-* [schonsense/70B_unstructWR](https://huggingface.co/schonsense/70B_unstructWR)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-models:
-  - model: schonsense/70B_unstructWR
-    parameters:
-      density: 0.7
-      epsilon: 0.2
-      weight: 0.4
-  - model: schonsense/SOG_10k_70B
-    parameters:
-      density: 0.7
-      epsilon: 0.2
-      weight: 0.5
-  - model: meta-llama/Llama-3.1-70B
-    parameters:
-      density: 0.8
-      epsilon: 0.1
-      weight: 0.1
-  - model: meta-llama/Llama-3.3-70B-Instruct
-merge_method: della
-base_model: meta-llama/Llama-3.3-70B-Instruct
-tokenizer_source: meta-llama/Llama-3.3-70B-Instruct
-parameters:
-  normalize: false
-  int8_mask: false
-  lambda: 1.0
-dtype: float32
-out_dtype: bfloat16
-```

+---
+base_model:
+- schonsense/SOG_10k_70B
+- meta-llama/Llama-3.1-70B
+- meta-llama/Llama-3.3-70B-Instruct
+- schonsense/70B_unstructWR
+library_name: transformers
+tags:
+- mergekit
+- merge
+---
+# 70B_SOG_unstructed
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317d4867690c5b55e61ce3d/Ffz7KZjg70xYP58TRhIiG.png)
+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 [DELLA](https://arxiv.org/abs/2406.11617) merge method using [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as a base.
+### Models Merged
+The following models were included in the merge:
+* [schonsense/SOG_10k_70B](https://huggingface.co/schonsense/SOG_10k_70B)
+* [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B)
+* [schonsense/70B_unstructWR](https://huggingface.co/schonsense/70B_unstructWR)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+models:
+  - model: schonsense/70B_unstructWR
+    parameters:
+      density: 0.7
+      epsilon: 0.2
+      weight: 0.4
+  - model: schonsense/SOG_10k_70B
+    parameters:
+      density: 0.7
+      epsilon: 0.2
+      weight: 0.5
+  - model: meta-llama/Llama-3.1-70B
+    parameters:
+      density: 0.8
+      epsilon: 0.1
+      weight: 0.1
+  - model: meta-llama/Llama-3.3-70B-Instruct
+merge_method: della
+base_model: meta-llama/Llama-3.3-70B-Instruct
+tokenizer_source: meta-llama/Llama-3.3-70B-Instruct
+parameters:
+  normalize: false
+  int8_mask: false
+  lambda: 1.0
+dtype: float32
+out_dtype: bfloat16
+```