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 1 day ago

Commit

a33cf4d

verified ·

1 Parent(s): 205133e

add judge_config.json

Browse files

Files changed (1) hide show

judge_config.json +104 -0

judge_config.json ADDED Viewed

	@@ -0,0 +1,104 @@

+{
+  "base_model_path": "/root/models/Qwen3.8-27B",
+  "hidden_size": 5120,
+  "slots": {
+    "num_slots": 24,
+    "ranges": {
+      "noul": [
+        0,
+        2
+      ],
+      "score": [
+        2,
+        8
+      ],
+      "choice": [
+        8,
+        24
+      ]
+    },
+    "verbalizers": [
+      "false",
+      "true",
+      "0",
+      "1",
+      "2",
+      "3",
+      "4",
+      "5",
+      "A",
+      "B",
+      "C",
+      "D",
+      "E",
+      "F",
+      "G",
+      "H",
+      "I",
+      "J",
+      "K",
+      "L",
+      "M",
+      "N",
+      "O",
+      "P"
+    ],
+    "template_version": "bare-v1"
+  },
+  "verbalizer_ids": [
+    3721,
+    1802,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    32,
+    33,
+    34,
+    35,
+    36,
+    37,
+    38,
+    39,
+    40,
+    41,
+    42,
+    43,
+    44,
+    45,
+    46,
+    47
+  ],
+  "kinds": [
+    "noul",
+    "choice",
+    "score"
+  ],
+  "model_name": "jev-judge-qwen38-27b",
+  "model_version": "0.8.0",
+  "lm_head_included": true,
+  "weights_mode": "unmerged",
+  "adapter_subfolder": "adapter",
+  "lora": {
+    "r": 16,
+    "alpha": 32,
+    "dropout": 0.05,
+    "target_modules": [
+      "in_proj_qkv",
+      "in_proj_z",
+      "q_proj",
+      "k_proj",
+      "v_proj",
+      "o_proj",
+      "gate_proj",
+      "up_proj",
+      "down_proj",
+      "out_proj"
+    ]
+  },
+  "trained_stage": "s2",
+  "source_checkpoint": "/root/jev-judge/checkpoints/s2_27b/best",
+  "softcap": null
+}