Instructions to use SargeDev/Solar-JEV-27B-R32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SargeDev/Solar-JEV-27B-R32 with Transformers:

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

pipe = pipeline("text-generation", model="SargeDev/Solar-JEV-27B-R32")
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("SargeDev/Solar-JEV-27B-R32")
model = AutoModelForCausalLM.from_pretrained("SargeDev/Solar-JEV-27B-R32", 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 SargeDev/Solar-JEV-27B-R32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SargeDev/Solar-JEV-27B-R32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SargeDev/Solar-JEV-27B-R32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SargeDev/Solar-JEV-27B-R32

SGLang

How to use SargeDev/Solar-JEV-27B-R32 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 "SargeDev/Solar-JEV-27B-R32" \
    --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": "SargeDev/Solar-JEV-27B-R32",
		"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 "SargeDev/Solar-JEV-27B-R32" \
        --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": "SargeDev/Solar-JEV-27B-R32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SargeDev/Solar-JEV-27B-R32 with Docker Model Runner:
```
docker model run hf.co/SargeDev/Solar-JEV-27B-R32
```

SargeDev commited on 17 days ago

Commit

84cffc8

verified ·

1 Parent(s): 2870824

fix dead corpus-v4 link -> solar-decisions-corpus-v4; add dataset to frontmatter

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -17,6 +17,7 @@ tags:
 - solar-jev
 datasets:
 - SargeDev/jev-distill-corpus-v3
 license: apache-2.0
 ---
@@ -204,7 +205,7 @@ reproduced here in full:
 - **SargeDev** — [jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3),
   the 740,957-row calibrated typed-decision corpus AutoTrust's JEV models were
   trained on (Apache-2.0), and the **solar r16** adapter trained here on
-  [jev-distill-corpus-v4](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v4)
   (the 1,219-step JEV System-1 run, 17-domain tokenized mix).
 - **Qwen** — [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) base
   (Apache-2.0).
@@ -216,7 +217,7 @@ the vision tower and serve as a plain `qwen3_5_text` causal LM, the VL build
 keeps the tower and serves as `qwen3_5` / `Qwen3_5ForConditionalGeneration`).
 - **Adapters** — this release ships two, and they are NOT interchangeable:
   - `solar r16` (alpha 32, 400 modules, **no `lm_head`**): trained 1,219 steps
-    on the JEV System-1 corpus (jev-distill-corpus-v4 tokenized mix: T1 gold
     domains + memory_relevance + 17-domain fill), KL+RPS decision-head
     distillation objective (AutoTrust-style), cosine LR 2e-4, KL_W 1.0,
     RPS_W 0.3. Merged with peft `merge_and_unload`; merge fidelity verified by

 - solar-jev
 datasets:
 - SargeDev/jev-distill-corpus-v3
+- SargeDev/solar-decisions-corpus-v4
 license: apache-2.0
 ---
 - **SargeDev** — [jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3),
   the 740,957-row calibrated typed-decision corpus AutoTrust's JEV models were
   trained on (Apache-2.0), and the **solar r16** adapter trained here on
+  [solar-decisions-corpus-v4](https://huggingface.co/datasets/SargeDev/solar-decisions-corpus-v4)
   (the 1,219-step JEV System-1 run, 17-domain tokenized mix).
 - **Qwen** — [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) base
   (Apache-2.0).
 keeps the tower and serves as `qwen3_5` / `Qwen3_5ForConditionalGeneration`).
 - **Adapters** — this release ships two, and they are NOT interchangeable:
   - `solar r16` (alpha 32, 400 modules, **no `lm_head`**): trained 1,219 steps
+    on the JEV System-1 corpus (solar-decisions-corpus-v4 tokenized mix: T1 gold
     domains + memory_relevance + 17-domain fill), KL+RPS decision-head
     distillation objective (AutoTrust-style), cosine LR 2e-4, KL_W 1.0,
     RPS_W 0.3. Merged with peft `merge_and_unload`; merge fidelity verified by