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

b26fadb

verified ·

1 Parent(s): 00114e0

add calibration.json

Browse files

Files changed (1) hide show

calibration.json +44 -0

calibration.json ADDED Viewed

	@@ -0,0 +1,44 @@

+{
+  "version": 1,
+  "per_kind": {
+    "noul": 1.0143134751376188,
+    "choice": 1.0161280671450397,
+    "score": 1.0036213515883572
+  },
+  "per_kind_family": {},
+  "fit": {
+    "noul": {
+      "n": 4363,
+      "T": 1.0143134751376188,
+      "kl_before": 0.004241107031702995,
+      "kl_after": 0.004223881289362907
+    },
+    "choice": {
+      "n": 3418,
+      "T": 1.0161280671450397,
+      "kl_before": 0.04535770043730736,
+      "kl_after": 0.04530283063650131
+    },
+    "score": {
+      "n": 3173,
+      "T": 1.0036213515883572,
+      "kl_before": 0.0210201907902956,
+      "kl_after": 0.021016595885157585
+    }
+  },
+  "diagnostic_calibration_split": {
+    "raw": {
+      "kl": 0.021931130439043045,
+      "ece": 0.002680546396815126,
+      "mce": 0.012234896421432495
+    },
+    "calibrated": {
+      "kl": 0.021906107664108276,
+      "ece": 0.0014270851053589991,
+      "mce": 0.01255643367767334
+    }
+  },
+  "source": "checkpoints/s2_27b/best",
+  "fit_rows": 10954,
+  "d1_excluded": true
+}