Instructions to use interview-eval/zephyr-7b-math-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use interview-eval/zephyr-7b-math-test with Transformers:

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

pipe = pipeline("text-generation", model="interview-eval/zephyr-7b-math-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("interview-eval/zephyr-7b-math-test")
model = AutoModelForCausalLM.from_pretrained("interview-eval/zephyr-7b-math-test")
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 interview-eval/zephyr-7b-math-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "interview-eval/zephyr-7b-math-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "interview-eval/zephyr-7b-math-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/interview-eval/zephyr-7b-math-test

SGLang

How to use interview-eval/zephyr-7b-math-test 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 "interview-eval/zephyr-7b-math-test" \
    --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": "interview-eval/zephyr-7b-math-test",
		"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 "interview-eval/zephyr-7b-math-test" \
        --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": "interview-eval/zephyr-7b-math-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use interview-eval/zephyr-7b-math-test with Docker Model Runner:
```
docker model run hf.co/interview-eval/zephyr-7b-math-test
```

Juyoung Suk commited on Sep 8, 2024

Commit

ebb8dbb

verified ·

1 Parent(s): b76a69c

End of training

Browse files

Files changed (4) hide show

README.md +6 -2
all_results.json +5 -0
config.json +1 -1
eval_results.json +8 -0

README.md CHANGED Viewed

@@ -3,11 +3,15 @@ library_name: transformers
 license: apache-2.0
 base_model: alignment-handbook/zephyr-7b-sft-full
 tags:
 - trl
 - sft
 - generated_from_trainer
 datasets:
-- generator
 model-index:
 - name: zephyr-7b-math-test
   results: []
@@ -18,7 +22,7 @@ should probably proofread and complete it, then remove this comment. -->
 # zephyr-7b-math-test
-This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the generator dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0178

 license: apache-2.0
 base_model: alignment-handbook/zephyr-7b-sft-full
 tags:
+- alignment-handbook
+- trl
+- sft
+- generated_from_trainer
 - trl
 - sft
 - generated_from_trainer
 datasets:
+- EunsuKim/MATH
 model-index:
 - name: zephyr-7b-math-test
   results: []
 # zephyr-7b-math-test
+This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the EunsuKim/MATH dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0178

all_results.json CHANGED Viewed

@@ -1,5 +1,10 @@
 {
     "epoch": 10.0,
     "total_flos": 10468982784000.0,
     "train_loss": 0.2729184678196907,
     "train_runtime": 352.114,

 {
     "epoch": 10.0,
+    "eval_loss": 0.017760511487722397,
+    "eval_runtime": 5.7156,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 54.412,
+    "eval_steps_per_second": 1.75,
     "total_flos": 10468982784000.0,
     "train_loss": 0.2729184678196907,
     "train_runtime": 352.114,

config.json CHANGED Viewed

@@ -22,6 +22,6 @@
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.44.2",
-  "use_cache": false,
   "vocab_size": 32000
 }

   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.44.2",
+  "use_cache": true,
   "vocab_size": 32000
 }

eval_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+    "epoch": 10.0,
+    "eval_loss": 0.017760511487722397,
+    "eval_runtime": 5.7156,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 54.412,
+    "eval_steps_per_second": 1.75
+}