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

608685e

verified ·

1 Parent(s): b26fadb

add decision_head.json

Browse files

Files changed (1) hide show

decision_head.json +99 -0

decision_head.json ADDED Viewed

	@@ -0,0 +1,99 @@

+{
+ "bias": [
+  0.00011960109259234741,
+  -0.00011981795250903815,
+  -0.001826660125516355,
+  -0.0008995002135634422,
+  1.2772755326295737e-05,
+  0.001714941579848528,
+  0.0009451315272599459,
+  -0.0020803064107894897,
+  -0.00017852929886430502,
+  -0.00010998119250871241,
+  -0.00011865551641676575,
+  7.452104182448238e-05,
+  -8.196464477805421e-05,
+  8.958959369920194e-06,
+  -0.002271531615406275,
+  -0.0069303084164857864,
+  -0.0064850919879972935,
+  -0.004779351409524679,
+  -0.002676677191630006,
+  -0.0025728552136570215,
+  -0.002822346054017544,
+  -0.002843661466613412,
+  -0.0031860454473644495,
+  -0.002381357364356518
+ ],
+ "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
+ ],
+ "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"
+ },
+ "note": "decision logits = lm_head-LoRA logprobs of verbalizer ids + bias, then per-kind temperature"
+}